A survey on parallel clustering algorithms for Big Data
Data clustering is one of the most studied data mining tasks. It aims, through various methods, to discover previously unknown groups within the data sets. In the past years, considerable progress has been made in this field leading to the development of innovative and promising clustering algorithm...
Uloženo v:
| Vydáno v: | The Artificial intelligence review Ročník 54; číslo 4; s. 2411 - 2443 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Dordrecht
Springer Netherlands
01.04.2021
Springer Springer Nature B.V |
| Témata: | |
| ISSN: | 0269-2821, 1573-7462 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Data clustering is one of the most studied data mining tasks. It aims, through various methods, to discover previously unknown groups within the data sets. In the past years, considerable progress has been made in this field leading to the development of innovative and promising clustering algorithms. These traditional clustering algorithms present some serious issues in connection with the speed-up, the throughput, and the scalability. Thus, they can no longer be directly used in the context of Big Data, where data are mainly characterized by their volume, velocity, and variety. In order to overcome their limitations, the research today is heading to the parallel computing concept by giving rise to the so-called parallel clustering algorithms. This paper presents an overview of the latest parallel clustering algorithms categorized according to the computing platforms used to handle the Big Data, namely, the horizontal and vertical scaling platforms. The former category includes peer-to-peer networks, MapReduce, and Spark platforms, while the latter category includes Multi-core processors, Graphics Processing Unit, and Field Programmable Gate Arrays platforms. In addition, it includes a comparison of the performance of the reviewed algorithms based on some common criteria of clustering validation in the Big Data context. Therefore, it provides the reader with an overall vision of the current parallel clustering techniques. |
|---|---|
| AbstractList | Data clustering is one of the most studied data mining tasks. It aims, through various methods, to discover previously unknown groups within the data sets. In the past years, considerable progress has been made in this field leading to the development of innovative and promising clustering algorithms. These traditional clustering algorithms present some serious issues in connection with the speed-up, the throughput, and the scalability. Thus, they can no longer be directly used in the context of Big Data, where data are mainly characterized by their volume, velocity, and variety. In order to overcome their limitations, the research today is heading to the parallel computing concept by giving rise to the so-called parallel clustering algorithms. This paper presents an overview of the latest parallel clustering algorithms categorized according to the computing platforms used to handle the Big Data, namely, the horizontal and vertical scaling platforms. The former category includes peer-to-peer networks, MapReduce, and Spark platforms, while the latter category includes Multi-core processors, Graphics Processing Unit, and Field Programmable Gate Arrays platforms. In addition, it includes a comparison of the performance of the reviewed algorithms based on some common criteria of clustering validation in the Big Data context. Therefore, it provides the reader with an overall vision of the current parallel clustering techniques. |
| Audience | Academic |
| Author | Lamari, Yasmine Slaoui, Said Chah Dafir, Zineb |
| Author_xml | – sequence: 1 givenname: Zineb orcidid: 0000-0002-9438-3262 surname: Dafir fullname: Dafir, Zineb email: zineb.dafir@um5s.net.ma organization: Faculty of Science of Rabat, Mohammed V University – sequence: 2 givenname: Yasmine surname: Lamari fullname: Lamari, Yasmine organization: Faculty of Science of Rabat, Mohammed V University – sequence: 3 givenname: Said Chah surname: Slaoui fullname: Slaoui, Said Chah organization: Faculty of Science of Rabat, Mohammed V University |
| BookMark | eNp9kE1LAzEQhoNUsK3-AU8Bz1vzsZtkj7V-QsGLnkM2TWpKulmTXaH_3tQVBA8lh4GZ98kwzwxM2tAaAK4xWmCE-G3CqGSkQAQVqK6xKMgZmOKK04Ln_gRMEWF1QQTBF2CW0g4hVJGSTgFfwjTEL3OAoYWdisp746H2Q-pNdO0WKr8N0fUf-wRtiPDObeG96tUlOLfKJ3P1W-fg_fHhbfVcrF-fXlbLdaFpJfoCq1JxoSqmkaUNVYJUNavtRnNNjSVNHjaW0boWbMMaJihjwpbMlhVrjOaMzsHN-G8Xw-dgUi93YYhtXilJhThlJIdyajGmtsob6Vob-qh0fhuzdzqrsi73lxxnASUSOANkBHQMKUVjZRfdXsWDxEgejcrRqMxG5Y9RSTIk_kHa9ap3oc3bnD-N0hFN3dGqiX9nnKC-AR4jitk |
| CitedBy_id | crossref_primary_10_1016_j_compbiomed_2025_111016 crossref_primary_10_1016_j_engappai_2022_104743 crossref_primary_10_3233_JCM_226152 crossref_primary_10_1109_ACCESS_2021_3084057 crossref_primary_10_1109_TSP_2025_3531292 crossref_primary_10_3389_fninf_2021_727859 crossref_primary_10_3390_math13142272 crossref_primary_10_1016_j_engappai_2023_107653 crossref_primary_10_3390_s23062952 crossref_primary_10_1108_JFC_11_2024_0356 crossref_primary_10_1109_JSTARS_2024_3521034 crossref_primary_10_1155_2021_8266436 crossref_primary_10_1016_j_jestch_2021_11_003 crossref_primary_10_1016_j_ins_2022_11_107 crossref_primary_10_1155_2022_5360302 crossref_primary_10_32604_cmc_2023_042222 crossref_primary_10_1002_wics_1566 crossref_primary_10_1007_s11280_023_01158_y crossref_primary_10_1109_ACCESS_2022_3164192 crossref_primary_10_3390_s24134276 crossref_primary_10_1049_rpg2_70006 crossref_primary_10_3390_math12131930 crossref_primary_10_3390_app13063529 crossref_primary_10_1016_j_heliyon_2023_e20468 crossref_primary_10_1016_j_ins_2023_119634 crossref_primary_10_1007_s10462_022_10310_5 crossref_primary_10_1080_15567036_2024_2334923 crossref_primary_10_1007_s10044_021_01045_0 crossref_primary_10_1007_s12530_022_09447_z crossref_primary_10_1007_s11227_021_04204_6 crossref_primary_10_1016_j_jocs_2022_101911 crossref_primary_10_1007_s12652_022_04428_1 crossref_primary_10_1016_j_eij_2025_100641 crossref_primary_10_1016_j_ins_2022_11_139 crossref_primary_10_1109_ACCESS_2024_3461798 crossref_primary_10_3390_electronics11060883 crossref_primary_10_1016_j_softx_2024_101666 crossref_primary_10_1145_3698875 crossref_primary_10_1007_s10489_022_03661_7 crossref_primary_10_1007_s12065_022_00720_3 crossref_primary_10_1007_s42044_022_00102_z crossref_primary_10_1016_j_jpdc_2025_105067 crossref_primary_10_3390_electronics13214215 crossref_primary_10_1016_j_jksuci_2024_101973 crossref_primary_10_1364_JOSAA_555355 |
| Cites_doi | 10.1016/j.jpdc.2011.03.007 10.1016/j.patrec.2016.07.027 10.1007/s10922-005-4441-x 10.1109/MCSE.2010.69 10.1016/j.sysarc.2006.02.003 10.1587/transinf.E95.D.2327 10.1145/304182.304187 10.1016/j.procs.2017.03.138 10.1109/WETICE.2016.26 10.1145/2487575.2487628 10.1007/s00500-013-1074-y 10.1109/JPROC.2008.917757 10.1186/2192-113X-2-18 10.1109/TPAMI.2002.1017616 10.1016/j.compeleceng.2017.12.002 10.6028/jres.045.026 10.1186/1471-2105-11-169 10.1016/j.procs.2013.05.200 10.1109/TBDATA.2016.2622288 10.3906/elk-1202-83 10.1145/2903220.2903255 10.1109/BDCloud-SocialCom-SustainCom.2016.85 10.1145/2724722 10.1145/2608029.2608033 10.1137/040608635 10.1109/PARELEC.2006.54 10.1109/IPDPSW.2010.5470922 10.1007/978-1-4614-3594-5_2 10.1007/978-3-319-27122-4_8 10.1016/B978-012722442-8/50016-1 10.1007/s11704-013-3158-3 10.1137/S1064827595287997 10.1109/AHS.2011.5963944 10.1109/HPCSim.2015.7237086 10.1109/TETC.2014.2330519 10.1089/106652799318274 10.1145/2020408.2020516 10.1109/HOTCHIPS.2008.7476525 10.1145/2503210.2503255 10.1109/TNS.2014.2364183 10.1007/s10586-014-0413-9 10.1016/j.jpdc.2012.06.009 10.1145/1851476.1851593 10.1007/s11227-014-1185-y 10.1007/s11227-014-1225-7 10.1109/IPDPSW.2016.57 10.1007/s007780050009 10.1109/TCBB.2011.68 10.1016/B978-0-12-381479-1.00010-1 10.1093/nar/30.7.1575 10.1016/j.procs.2016.05.389 10.1186/s40537-014-0008-6 10.1145/2851613.2851643 10.1109/TFUZZ.2012.2201485 10.1007/s11227-014-1174-1 10.1109/FPL.2013.6645501 10.1007/978-1-4615-3572-0 10.1016/j.asoc.2016.11.045 10.1007/s10844-006-9953-7 10.1109/4434.806976 10.2307/2346830 10.1109/CBD.2016.016 10.1145/2806416.2806545 10.1109/ICCT.2012.6511380 10.1145/1081870.1081955 10.1007/978-3-540-77220-0_21 10.1109/ICACCI.2016.7732323 10.1109/SMC.2013.480 10.1007/978-3-642-10665-1_71 10.1016/j.patcog.2015.02.020 10.1145/79173.79181 10.1109/BigData.2016.7840708 10.1126/science.1242072 10.1007/978-3-319-48472-3_20 10.1109/IPDPS.2010.5470429 |
| ContentType | Journal Article |
| Copyright | Springer Nature B.V. 2020 COPYRIGHT 2021 Springer Copyright Springer Nature B.V. Apr 2021 |
| Copyright_xml | – notice: Springer Nature B.V. 2020 – notice: COPYRIGHT 2021 Springer – notice: Copyright Springer Nature B.V. Apr 2021 |
| DBID | AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL ABUWG AFKRA ALSLI ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU CNYFK DWQXO E3H F2A FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N M1O P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PRINS PRQQA PSYQQ Q9U |
| DOI | 10.1007/s10462-020-09918-2 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Global (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Social Science Premium Collection Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Business Premium Collection ProQuest Technology Collection ProQuest One ProQuest Social Science Premium Collection - Library & Information Science Collection ProQuest Central Korea Library & Information Sciences Abstracts (LISA) Library & Information Science Abstracts (LISA) Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Library Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Social Sciences ProQuest One Psychology ProQuest Central Basic |
| DatabaseTitle | CrossRef ProQuest Business Collection (Alumni Edition) ProQuest One Psychology Computer Science Database ProQuest Central Student Library and Information Science Abstracts (LISA) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ABI/INFORM Complete ProQuest One Applied & Life Sciences Library & Information Science Collection ProQuest Central (New) Advanced Technologies & Aerospace Collection Business Premium Collection Social Science Premium Collection ABI/INFORM Global ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest Business Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ABI/INFORM Global (Corporate) ProQuest One Business Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Pharma Collection ProQuest Central ABI/INFORM Professional Advanced ProQuest Library Science ProQuest Central Korea Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) ProQuest Computing ProQuest One Social Sciences ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Business (Alumni) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | ProQuest Business Collection (Alumni Edition) |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1573-7462 |
| EndPage | 2443 |
| ExternalDocumentID | A718214081 10_1007_s10462_020_09918_2 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 1N0 1SB 2.D 203 23N 28- 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6J9 6NX 77K 7WY 8AO 8FE 8FG 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AAHNG AAIAL AAJKR AAJSJ AAKKN AANZL AAOBN AARHV AARTL AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABEEZ ABFTD ABFTV ABHLI ABHQN ABIVO ABJNI ABJOX ABKCH ABKTR ABMNI ABMOR ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACACY ACBXY ACGFS ACHSB ACHXU ACIHN ACKNC ACMDZ ACMLO ACOKC ACOMO ACREN ACSNA ACULB ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFFNX AFGCZ AFGXO AFKRA AFLOW AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALSLI ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ C24 C6C CAG CCPQU CNYFK COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DWQXO EBLON EBS EDO EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IAO IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW LAK LLZTM M0C M0N M1O M4Y MA- MK~ N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PSYQQ PT5 Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WH7 WK8 YLTOR Z45 Z5O Z7R Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~A9 ~EX 77I AAFWJ AASML AAYXX ABDBE ABFSG ACSTC ADHKG AEZWR AFFHD AFHIU AGQPQ AHPBZ AHWEU AIXLP AYFIA CITATION ICD PHGZM PHGZT PQGLB PRQQA 7SC 7XB 8AL 8FD 8FK E3H F2A JQ2 L.- L7M L~C L~D PKEHL PQEST PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c358t-1a4a78a56c0f3b3a825969fdc7c3ef2ba78bf639986d6b683668f46f456bec763 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 60 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000575762200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0269-2821 |
| IngestDate | Sat Nov 15 08:11:31 EST 2025 Sat Nov 29 09:49:08 EST 2025 Sat Nov 29 02:43:25 EST 2025 Tue Nov 18 22:34:16 EST 2025 Fri Feb 21 02:49:25 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | means Algorithms FPGA MPI Big Data DBSCAN Spark Clustering Data mining GPU Multi-cores CPU MapReduce |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c358t-1a4a78a56c0f3b3a825969fdc7c3ef2ba78bf639986d6b683668f46f456bec763 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-9438-3262 |
| PQID | 2507362763 |
| PQPubID | 36790 |
| PageCount | 33 |
| ParticipantIDs | proquest_journals_2507362763 gale_infotracacademiconefile_A718214081 crossref_primary_10_1007_s10462_020_09918_2 crossref_citationtrail_10_1007_s10462_020_09918_2 springer_journals_10_1007_s10462_020_09918_2 |
| PublicationCentury | 2000 |
| PublicationDate | 20210400 2021-04-00 20210401 |
| PublicationDateYYYYMMDD | 2021-04-01 |
| PublicationDate_xml | – month: 4 year: 2021 text: 20210400 |
| PublicationDecade | 2020 |
| PublicationPlace | Dordrecht |
| PublicationPlace_xml | – name: Dordrecht |
| PublicationSubtitle | An International Science and Engineering Journal |
| PublicationTitle | The Artificial intelligence review |
| PublicationTitleAbbrev | Artif Intell Rev |
| PublicationYear | 2021 |
| Publisher | Springer Netherlands Springer Springer Nature B.V |
| Publisher_xml | – name: Springer Netherlands – name: Springer – name: Springer Nature B.V |
| References | AkhterSRobertsJMulti-core programming: increasing performance through software multi-threading20061HillsboroIntel Press Harish P, Narayanan PJ (2007) Accelerating large graph algorithms on the GPU using CUDA. In: High performance computing—HiPC 2007. Lecture notes in computer science. Springer, Berlin, pp 197–208. https://doi.org/10.1007/978-3-540-77220-0_21 Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX conference on hot topics in cloud computing, HotCloud’10. USENIX Association, Berkeley Zhao W, Ma H, He Q (2009) Parallel k-means clustering based on MapReduce. In: Cloud computing. Lecture notes in computer science. Springer, Berlin, pp 674–679. https://doi.org/10.1007/978-3-642-10665-1_71 SinghDReddyCKA survey on platforms for big data analyticsJ Big Data201421810.1186/s40537-014-0008-6 YanWBrahmakshatriyaUXueYGilderMWiseBp-PIC: parallel power iteration clustering for big dataJ Parallel Distrib Comput201373335235910.1016/j.jpdc.2012.06.009 Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining, KDD’96. AAAI Press, Portland, pp 226–231 HavensTCBezdekJCLeckieCHallLOPalaniswamiMFuzzy c-means algorithms for very large dataIEEE Trans Fuzzy Syst20122061130114610.1109/TFUZZ.2012.2201485 BharillNTiwariAMalviyaAFuzzy based scalable clustering algorithms for handling big data using apache sparkIEEE Trans Big Data20162433935210.1109/TBDATA.2016.2622288 HeYTanHLuoWFengSFanJMR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed dataFront Comput Sci2014818399315159110.1007/s11704-013-3158-3 HadianAShahrivariSHigh performance parallel k-means clustering for disk-resident datasets on multi-core CPUsJ Supercomput201469284586310.1007/s11227-014-1185-y Nickolls J, Buck I, Garland M (2008) Scalable parallel programming. In: IEEE hot chips 20 symposium (HCS). IEEE, pp 40–53 CuomoSDe AngelisVFarinaGMarcellinoLToraldoGA GPU-accelerated parallel k-means algorithmComput Electr Eng201710.1016/j.compeleceng.2017.12.002 Farooq U, Marrakchi Z, Mehrez H (2012) FPGA architectures: an overview. In: Tree-based heterogeneous FPGA architectures, chap. 2. Springer, New York, pp 7–48. https://doi.org/10.1007/978-1-4614-3594-5_2 Tsapanos N, Tefas A, Nikolaidis N, Pitas I (2016) Efficient mapreduce kernel k-means for big data clustering. In: Proceedings of the 9th hellenic conference on artificial intelligence, SETN ’16. ACM, Thessaloniki, pp 28:1–28:5. https://doi.org/10.1145/2903220.2903255 Aggarwal CC, Han J, Wang J, Yu PS (2003) A framework for clustering evolving data streams. In: Proceedings of the 29th international conference on very large data bases, VLDB ’03, vol 29. VLDB Endowment, Berlin, pp 81–92 BustamamABurrageKHamiltonNAFast parallel markov clustering in bioinformatics using massively parallel computing on GPU with CUDA and ELLPACK-R sparse formatIEEE/ACM Trans Comput Biol Bioinform20129367969210.1109/TCBB.2011.68 Savvas IK, Tselios D (2016) Parallelizing DBSCAN algorithm using MPI. In: IEEE 25th International conference on enabling technologies: infrastructure for collaborative enterprises (WETICE). IEEE, Paris, pp 77–82. https://doi.org/10.1109/WETICE.2016.26 SkillicornDStrategies for parallel data miningIEEE Concurr199974263510.1109/4434.806976 DengZHuYZhuMHuangXDuBA scalable and fast optics for clustering trajectory big dataCluster Comput201518254956210.1007/s10586-014-0413-9 FahadAAlshatriNTariZAlamriAKhalilIZomayaAYFoufouSBourasAA survey of clustering algorithms for Big Data: taxonomy and empirical analysisIEEE Trans Emerg Top Comput20142326727910.1109/TETC.2014.2330519 Ben-DorAShamirRYakhiniZClustering gene expression patternsJ Comput Biol199963–428129710.1089/106652799318274 KarypisGKumarVA fast and high quality multilevel scheme for partitioning irregular graphsSIAM J Sci Comput1998201359392163907310.1137/S10648275952879970915.68129 Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) Optics: ordering points to identify the clustering structure. In: Proceedings of the 1999 ACM SIGMOD international conference on management of data, SIGMOD ’99. ACM, Philadelphia, pp 49–60. https://doi.org/10.1145/304182.304187 Mallios X, Vassalos V, Venetis T, Vlachou A (2016) A framework for clustering and classification of big data using spark. In: Debruyne C, Panetto H, Meersman R, Dillon T, Kühn E, O’Sullivan D, Ardagna CA (eds) On the move to meaningful internet systems: OTM 2016 conferences, vol 10033. Springer, Cham, pp 344–362. https://doi.org/10.1007/978-3-319-48472-3_20 Ferreira Cordeiro RL, Traina Junior C, Machado Traina AJ, López J, Kang U, Faloutsos C (2011) Clustering very large multi-dimensional datasets with mapreduce. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’11. ACM, San Diego, pp 690–698. https://doi.org/10.1145/2020408.2020516 Van DongenSGraph clustering via a discrete uncoupling processSIAM J Matrix Anal Appl2008301121141239957210.1137/0406086351161.68041 AzimiRSajediHGhayekhlooMA distributed data clustering algorithm in p2p networksAppl Soft Comput201751Supplement C14716710.1016/j.asoc.2016.11.045 Laney D (2001) 3D data management: controlling data volume, velocity, and variety. Technical Report, 949, Gartner ShiSYueQWangQFPGA based accelerator for parallel DBSCAN algorithmComput Model New Technol2014182135142 Wang B, Yin J, Hua Q, Wu Z, Cao J (2016) Parallelizing k-means-based clustering on spark. In: International conference on advanced cloud and Big Data (CBD). IEEE, Chengdu, pp 31–36. https://doi.org/10.1109/CBD.2016.016 Kriegel HP, Pfeifle M (2005) Density-based clustering of uncertain data. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining. ACM, Chicago, pp 672–677. https://doi.org/10.1145/1081870.1081955 SunZFoxGGuWLiZA parallel clustering method combined information bottleneck theory and centroid-based clusteringJ Supercomput201469145246710.1007/s11227-014-1174-1 Hussain HM, Benkrid K, Seker H, Erdogan AT (2011) FPGA implementation of k-means algorithm for bioinformatics application: an accelerated approach to clustering microarray data. In: NASA/ESA conference on adaptive hardware and systems (AHS). IEEE, San Diego, pp 248–255. https://doi.org/10.1109/AHS.2011.5963944 HartiganJAWongMAAlgorithm as 136: a k-means clustering algorithmAppl Stat197928110010.2307/23468300447.62062 AndradeGRamosGMadeiraDSachettoRFerreiraRRochaLG-DBSCAN: a GPU accelerated algorithm for density-based clusteringProcedia Comput Sci201318Supplement C36937810.1016/j.procs.2013.05.200 Lin F, Cohen WW (2010) Power iteration clustering. In: Proceedings of the 27th international conference on machine learning (ICML-10). Omnipress, Haifa, pp 655–662 Ekanayake J, Li H, Zhang B, Gunarathne T, Bae SH, Qiu J, Fox G (2010) Twister: a runtime for iterative MapReduce. In: Proceedings of the 19th ACM international symposium on high performance distributed computing. ACM, pp 810–818 Gouineau F, Landry T, Triplet T (2016) Patchwork, a scalable density-grid clustering algorithm. In: Proceedings of the 31st annual ACM symposium on applied computing, SAC ’16. ACM, Pisa, pp 824–831. https://doi.org/10.1145/2851613.2851643 Yang J, Li X (2013) MapReduce based method for big data semantic clustering. In: IEEE international conference on systems, man, and cybernetics. IEEE, pp 2814–2819. https://doi.org/10.1109/SMC.2013.480 NanniMPedreschiDTime-focused clustering of trajectories of moving objectsJ Intell Inf Syst200627326728910.1007/s10844-006-9953-7 SheikholeslamiGChatterjeeSZhangAWavecluster: a wavelet-based clustering approach for spatial data in very large databasesVLDB J Int J Very Large Data Bases200083–428930410.1007/s007780050009 Patwary MA, Palsetia D, Agrawal A, Liao WK, Manne F, Choudhary A (2013) Scalable parallel optics data clustering using graph algorithmic techniques. In: Proceedings of the international conference on high performance computing, networking, storage and analysis, SC ’13. ACM, Denver, pp 49:1–49:12. https://doi.org/10.1145/2503210.2503255 Luo G, Luo X, Gooch TF, Tian L, Qin K (2016) A parallel DBSCAN algorithm based on spark. In: IEEE international conferences on big data and cloud computing, social computing and networking, sustainable computing and communications. IEEE, Atlanta, pp 548–553. https://doi.org/10.1109/BDCloud-SocialCom-SustainCom.2016.85 SotiropoulouCLGkaitatzisSAnnoviABerettaMGiannettiPKordasKLucianoPNikolaidisSPetridouCVolpiGA multi-core FPGA-based 2D-clustering implementation for real-time image processingIEEE Trans Nuclear Sci20146163599360610.1109/TNS.2014.2364183 StoneJEGoharaDShiGOpenCL: a parallel programming standard for heterogeneous computing systemsComput Sci Eng20101236610.1109/MCSE.2010.69 KrausJMKestlerHAA highly efficient multi-core algorithm for clustering extremely large datasetsBMC Bioinform201011116910.1186/1471-2105-11-169 VoulgarisSGavidiaDvan SteenMCyclon: inexpensive membership management for unstructured p2p overlaysJ Netw Syst Manag200513219721710.1007/s10922-005-4441-x Han J, Kamber M, Pei J (2012) Cluster analysis: basic concepts and methods. In: Data mining, The Morgan Kaufmann series in data management systems, 3rd edn, chap. 10. Morgan Kaufmann, pp 443–495. https://doi.org/10.1016/B978-0-12-381479-1.00010-1 AnFKoideTMattauschHJA k-means-based multi-prototype high-speed learning system with FPGA-implemented coprocessor for 1-NN searchingIEICE Trans Inf Syst2012E95–D92327233810.1587/transinf.E95.D.2327 Gehweiler J, Meyerhenke H (2010) A distributed diffusive heuristic for clustering a virtual p2p supercomputer. In: IEEE international symposium on parallel distributed processing, workshops and Phd forum (IPDPSW). IEEE, Atlanta, pp 1–8. https://doi.org/10.1109/IPDPSW.2010.5470922 LiuRLiXDu A Erdem (9918_CR21) 2014; 22 AA Yıldırım (9918_CR79) 2011; 71 D Singh (9918_CR63) 2014; 2 KW Lin (9918_CR46) 2014; 18 9918_CR59 W Yan (9918_CR77) 2013; 73 JE Stone (9918_CR67) 2010; 12 9918_CR13 X Cui (9918_CR14) 2006; 52 9918_CR57 9918_CR19 9918_CR17 9918_CR50 9918_CR51 9918_CR55 9918_CR53 JA Hartigan (9918_CR33) 1979; 28 A Fahad (9918_CR23) 2014; 2 R Jin (9918_CR39) 2013; 2 X Cui (9918_CR15) 2014; 70 SD Brown (9918_CR11) 1992 9918_CR25 A Hadian (9918_CR29) 2014; 69 9918_CR26 Y He (9918_CR35) 2014; 8 9918_CR24 N Tsapanos (9918_CR69) 2015; 48 A Bustamam (9918_CR12) 2012; 9 9918_CR27 9918_CR28 S Shi (9918_CR62) 2014; 18 R Liu (9918_CR48) 2017; 107 G Andrade (9918_CR5) 2013; 18 9918_CR22 G Sheikholeslami (9918_CR61) 2000; 8 9918_CR64 Z Deng (9918_CR18) 2015; 18 R Azimi (9918_CR7) 2017; 51 LG Valiant (9918_CR71) 1990; 33 9918_CR80 CL Sotiropoulou (9918_CR66) 2014; 61 TC Havens (9918_CR34) 2012; 20 A Banharnsakun (9918_CR8) 2017; 93 S Cuomo (9918_CR16) 2017 9918_CR36 9918_CR37 9918_CR78 S Akhter (9918_CR2) 2006 A Ben-Dor (9918_CR9) 1999; 6 9918_CR38 M Nanni (9918_CR54) 2006; 27 9918_CR70 9918_CR32 D Skillicorn (9918_CR65) 1999; 7 9918_CR76 9918_CR30 9918_CR74 9918_CR31 S Voulgaris (9918_CR73) 2005; 13 9918_CR75 T Kanungo (9918_CR40) 2002; 24 N Bharill (9918_CR10) 2016; 2 J Owens (9918_CR56) 2008; 96 9918_CR3 9918_CR6 9918_CR1 G Karypis (9918_CR41) 1998; 20 9918_CR47 9918_CR45 S Van Dongen (9918_CR72) 2008; 30 F An (9918_CR4) 2012; E95–D Z Sun (9918_CR68) 2014; 69 9918_CR49 D Melo (9918_CR52) 2016; 80 9918_CR83 N Scicluna (9918_CR60) 2015; 9 A Rodriguez (9918_CR58) 2014; 344 9918_CR81 AJ Enright (9918_CR20) 2002; 30 JM Kraus (9918_CR42) 2010; 11 9918_CR82 9918_CR43 9918_CR44 |
| References_xml | – reference: SheikholeslamiGChatterjeeSZhangAWavecluster: a wavelet-based clustering approach for spatial data in very large databasesVLDB J Int J Very Large Data Bases200083–428930410.1007/s007780050009 – reference: Wang B, Yin J, Hua Q, Wu Z, Cao J (2016) Parallelizing k-means-based clustering on spark. In: International conference on advanced cloud and Big Data (CBD). IEEE, Chengdu, pp 31–36. https://doi.org/10.1109/CBD.2016.016 – reference: Aggarwal CC, Han J, Wang J, Yu PS (2003) A framework for clustering evolving data streams. In: Proceedings of the 29th international conference on very large data bases, VLDB ’03, vol 29. VLDB Endowment, Berlin, pp 81–92 – reference: HadianAShahrivariSHigh performance parallel k-means clustering for disk-resident datasets on multi-core CPUsJ Supercomput201469284586310.1007/s11227-014-1185-y – reference: HavensTCBezdekJCLeckieCHallLOPalaniswamiMFuzzy c-means algorithms for very large dataIEEE Trans Fuzzy Syst20122061130114610.1109/TFUZZ.2012.2201485 – reference: Altinigneli MC, Plant C, Böhm C (2013) Massively parallel expectation maximization using graphics processing units. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’13. ACM, Chicago, pp 838–846. https://doi.org/10.1145/2487575.2487628 – reference: Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) Optics: ordering points to identify the clustering structure. In: Proceedings of the 1999 ACM SIGMOD international conference on management of data, SIGMOD ’99. ACM, Philadelphia, pp 49–60. https://doi.org/10.1145/304182.304187 – reference: MeloDToledoSMouraoFSachettoRAndradeGFerreiraRParthasarathySRochaLHierarchical density-based clustering based on GPU accelerated data indexing strategyProcedia Comput Sci20168095196110.1016/j.procs.2016.05.389 – reference: RodriguezALaioAClustering by fast search and find of density peaksScience201434461911492149610.1126/science.1242072 – reference: CuomoSDe AngelisVFarinaGMarcellinoLToraldoGA GPU-accelerated parallel k-means algorithmComput Electr Eng201710.1016/j.compeleceng.2017.12.002 – reference: SciclunaNBouganisCSARC 2014: a multidimensional FPGA-based parallel DBSCAN architectureACM Trans Reconfig Technol Syst2015912:12:1510.1145/2724722 – reference: Lanczos C (1950) An iteration method for the solution of the eigenvalue problem of linear differential and integral operators. United States Governm., Press Office Los Angeles – reference: Luo G, Luo X, Gooch TF, Tian L, Qin K (2016) A parallel DBSCAN algorithm based on spark. In: IEEE international conferences on big data and cloud computing, social computing and networking, sustainable computing and communications. IEEE, Atlanta, pp 548–553. https://doi.org/10.1109/BDCloud-SocialCom-SustainCom.2016.85 – reference: Ekanayake J, Li H, Zhang B, Gunarathne T, Bae SH, Qiu J, Fox G (2010) Twister: a runtime for iterative MapReduce. In: Proceedings of the 19th ACM international symposium on high performance distributed computing. ACM, pp 810–818 – reference: BharillNTiwariAMalviyaAFuzzy based scalable clustering algorithms for handling big data using apache sparkIEEE Trans Big Data20162433935210.1109/TBDATA.2016.2622288 – reference: Huang P, Li X, Yuan B (2015) A parallel gpu-based approach to clustering very fast data streams. In: Proceedings of the 24th ACM international on conference on information and knowledge management, CIKM ’15. ACM, Melbourne, pp 23–32. https://doi.org/10.1145/2806416.2806545 – reference: Wang J, Yuan D, Jiang M (2012) Parallel K-PSO based on MapReduce. In: IEEE 14th international conference on communication technology, pp 1203–1208. IEEE, Chengdu. https://doi.org/10.1109/ICCT.2012.6511380 – reference: Han D, Agrawal A, Liao WK, Choudhary A (2016) A novel scalable DBSCAN algorithm with Spark. In: IEEE international parallel and distributed processing symposium workshops (IPDPSW). IEEE, Chicago, pp 1393–1402. https://doi.org/10.1109/IPDPSW.2016.57 – reference: Harish P, Narayanan PJ (2007) Accelerating large graph algorithms on the GPU using CUDA. In: High performance computing—HiPC 2007. Lecture notes in computer science. Springer, Berlin, pp 197–208. https://doi.org/10.1007/978-3-540-77220-0_21 – reference: VoulgarisSGavidiaDvan SteenMCyclon: inexpensive membership management for unstructured p2p overlaysJ Netw Syst Manag200513219721710.1007/s10922-005-4441-x – reference: Ben-DorAShamirRYakhiniZClustering gene expression patternsJ Comput Biol199963–428129710.1089/106652799318274 – reference: Hussain HM, Benkrid K, Seker H, Erdogan AT (2011) FPGA implementation of k-means algorithm for bioinformatics application: an accelerated approach to clustering microarray data. In: NASA/ESA conference on adaptive hardware and systems (AHS). IEEE, San Diego, pp 248–255. https://doi.org/10.1109/AHS.2011.5963944 – reference: LiuRLiXDuLZhiSWeiMParallel implementation of density peaks clustering algorithm based on sparkProcedia Comput Sci2017107Supplement C44244710.1016/j.procs.2017.03.138 – reference: Nickolls J, Buck I, Garland M (2008) Scalable parallel programming. In: IEEE hot chips 20 symposium (HCS). IEEE, pp 40–53 – reference: TsapanosNTefasANikolaidisNPitasIA distributed framework for trimmed kernel k-means clusteringPattern Recognit20154882685269810.1016/j.patcog.2015.02.0201394.68315 – reference: FahadAAlshatriNTariZAlamriAKhalilIZomayaAYFoufouSBourasAA survey of clustering algorithms for Big Data: taxonomy and empirical analysisIEEE Trans Emerg Top Comput20142326727910.1109/TETC.2014.2330519 – reference: KanungoTMountDMNetanyahuNSPiatkoCDSilvermanRWuAYAn efficient k-means clustering algorithm: analysis and implementationIEEE Trans Pattern Anal Mach Intell200224788189210.1109/TPAMI.2002.10176161414.68128 – reference: BrownSDFrancisRJRoseJVranesicZGField-programmable gate arrays1992BostonSpringer10.1007/978-1-4615-3572-00780.68114 – reference: Gouineau F, Landry T, Triplet T (2016) Patchwork, a scalable density-grid clustering algorithm. In: Proceedings of the 31st annual ACM symposium on applied computing, SAC ’16. ACM, Pisa, pp 824–831. https://doi.org/10.1145/2851613.2851643 – reference: Zhao W, Ma H, He Q (2009) Parallel k-means clustering based on MapReduce. In: Cloud computing. Lecture notes in computer science. Springer, Berlin, pp 674–679. https://doi.org/10.1007/978-3-642-10665-1_71 – reference: HartiganJAWongMAAlgorithm as 136: a k-means clustering algorithmAppl Stat197928110010.2307/23468300447.62062 – reference: OwensJHoustonMLuebkeDGreenSStoneJPhillipsJGPU computingProc IEEE200896587989910.1109/JPROC.2008.917757 – reference: Patwary MA, Palsetia D, Agrawal A, Liao WK, Manne F, Choudhary A (2013) Scalable parallel optics data clustering using graph algorithmic techniques. In: Proceedings of the international conference on high performance computing, networking, storage and analysis, SC ’13. ACM, Denver, pp 49:1–49:12. https://doi.org/10.1145/2503210.2503255 – reference: YıldırımAAÖzdoğanCParallel wavecluster: a linear scaling parallel clustering algorithm implementation with application to very large datasetsJ Parallel Distrib Comput201171795596210.1016/j.jpdc.2011.03.007 – reference: Farooq U, Marrakchi Z, Mehrez H (2012) FPGA architectures: an overview. In: Tree-based heterogeneous FPGA architectures, chap. 2. Springer, New York, pp 7–48. https://doi.org/10.1007/978-1-4614-3594-5_2 – reference: CuiXZhuPYangXLiKJiCOptimized big data k-means clustering using MapReduceJ Supercomput20147031249125910.1007/s11227-014-1225-7 – reference: Yang J, Li X (2013) MapReduce based method for big data semantic clustering. In: IEEE international conference on systems, man, and cybernetics. IEEE, pp 2814–2819. https://doi.org/10.1109/SMC.2013.480 – reference: Zayani A, Ben N’Cir CE, Essoussi N (2016) Parallel clustering method for non-disjoint partitioning of large-scale data based on spark framework. In: IEEE international conference on big data (Big Data). IEEE, Washington, DC, pp 1064–1069. https://doi.org/10.1109/BigData.2016.7840708 – reference: Van DongenSGraph clustering via a discrete uncoupling processSIAM J Matrix Anal Appl2008301121141239957210.1137/0406086351161.68041 – reference: Lin F, Cohen WW (2010) Power iteration clustering. In: Proceedings of the 27th international conference on machine learning (ICML-10). Omnipress, Haifa, pp 655–662 – reference: AzimiRSajediHGhayekhlooMA distributed data clustering algorithm in p2p networksAppl Soft Comput201751Supplement C14716710.1016/j.asoc.2016.11.045 – reference: Sinha A, Jana PK (2016) A novel k-means based clustering algorithm for big data. In: International conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 1875–1879. https://doi.org/10.1109/ICACCI.2016.7732323 – reference: LinKWLinCHHsiaoCYA parallel and scalable cast-based clustering algorithm on GPUSoft Comput201418353954710.1007/s00500-013-1074-y – reference: SkillicornDStrategies for parallel data miningIEEE Concurr199974263510.1109/4434.806976 – reference: AndradeGRamosGMadeiraDSachettoRFerreiraRRochaLG-DBSCAN: a GPU accelerated algorithm for density-based clusteringProcedia Comput Sci201318Supplement C36937810.1016/j.procs.2013.05.200 – reference: Gehweiler J, Meyerhenke H (2010) A distributed diffusive heuristic for clustering a virtual p2p supercomputer. In: IEEE international symposium on parallel distributed processing, workshops and Phd forum (IPDPSW). IEEE, Atlanta, pp 1–8. https://doi.org/10.1109/IPDPSW.2010.5470922 – reference: Savvas IK, Tselios D (2016) Parallelizing DBSCAN algorithm using MPI. In: IEEE 25th International conference on enabling technologies: infrastructure for collaborative enterprises (WETICE). IEEE, Paris, pp 77–82. https://doi.org/10.1109/WETICE.2016.26 – reference: SinghDReddyCKA survey on platforms for big data analyticsJ Big Data201421810.1186/s40537-014-0008-6 – reference: CuiXGaoJPotokTEA flocking based algorithm for document clustering analysisJ Syst Archit200652850551510.1016/j.sysarc.2006.02.003 – reference: Han J, Kamber M, Pei J (2012) Cluster analysis: basic concepts and methods. In: Data mining, The Morgan Kaufmann series in data management systems, 3rd edn, chap. 10. Morgan Kaufmann, pp 443–495. https://doi.org/10.1016/B978-0-12-381479-1.00010-1 – reference: Laney D (2001) 3D data management: controlling data volume, velocity, and variety. Technical Report, 949, Gartner – reference: KarypisGKumarVA fast and high quality multilevel scheme for partitioning irregular graphsSIAM J Sci Comput1998201359392163907310.1137/S10648275952879970915.68129 – reference: Zhang Y, Mueller F, Cui X, Potok T (2010) Large-scale multi-dimensional document clustering on GPU clusters. In: IEEE international symposium on parallel distributed processing (IPDPS). IEEE, pp 1–10. https://doi.org/10.1109/IPDPS.2010.5470429 – reference: Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th conference on symposium on opearting systems design and implementation, OSDI’04, vol 6. USENIX Association, Berkeley – reference: KrausJMKestlerHAA highly efficient multi-core algorithm for clustering extremely large datasetsBMC Bioinform201011116910.1186/1471-2105-11-169 – reference: AnFKoideTMattauschHJA k-means-based multi-prototype high-speed learning system with FPGA-implemented coprocessor for 1-NN searchingIEICE Trans Inf Syst2012E95–D92327233810.1587/transinf.E95.D.2327 – reference: EnrightAJVan DongenSOuzounisCAAn efficient algorithm for large-scale detection of protein familiesNucleic Acids Res20023071575158410.1093/nar/30.7.1575 – reference: Ferreira Cordeiro RL, Traina Junior C, Machado Traina AJ, López J, Kang U, Faloutsos C (2011) Clustering very large multi-dimensional datasets with mapreduce. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’11. ACM, San Diego, pp 690–698. https://doi.org/10.1145/2020408.2020516 – reference: Milojicic DS, Kalogeraki V, Lukose R, Nagaraja K, Pruyne J, Richard B, Rollins S, Xu Z (2002) Peer-to-peer computing. Technical Report. HPL-2002-57, HP Labs – reference: Cordova I, Moh TS (2015) DBSCAN on resilient distributed datasets. In: International conference on high performance computing simulation (HPCS). IEEE, Amsterdam, pp 531–540. https://doi.org/10.1109/HPCSim.2015.7237086 – reference: StoneJEGoharaDShiGOpenCL: a parallel programming standard for heterogeneous computing systemsComput Sci Eng20101236610.1109/MCSE.2010.69 – reference: Mallios X, Vassalos V, Venetis T, Vlachou A (2016) A framework for clustering and classification of big data using spark. In: Debruyne C, Panetto H, Meersman R, Dillon T, Kühn E, O’Sullivan D, Ardagna CA (eds) On the move to meaningful internet systems: OTM 2016 conferences, vol 10033. Springer, Cham, pp 344–362. https://doi.org/10.1007/978-3-319-48472-3_20 – reference: SunZFoxGGuWLiZA parallel clustering method combined information bottleneck theory and centroid-based clusteringJ Supercomput201469145246710.1007/s11227-014-1174-1 – reference: HeYTanHLuoWFengSFanJMR-DBSCAN: a scalable MapReduce-based DBSCAN algorithm for heavily skewed dataFront Comput Sci2014818399315159110.1007/s11704-013-3158-3 – reference: BustamamABurrageKHamiltonNAFast parallel markov clustering in bioinformatics using massively parallel computing on GPU with CUDA and ELLPACK-R sparse formatIEEE/ACM Trans Comput Biol Bioinform20129367969210.1109/TCBB.2011.68 – reference: BanharnsakunAA mapreduce-based artificial bee colony for large-scale data clusteringPattern Recognit Lett201793Supplement C788410.1016/j.patrec.2016.07.027 – reference: Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining, KDD’96. AAAI Press, Portland, pp 226–231 – reference: ErdemAGündemTİM-FDBSCAN: a multicore density-based uncertain data clustering algorithmTurk J Electr Eng Comput Sci20142214315410.3906/elk-1202-83 – reference: Gepner P, Kowalik MF (2006) Multi-core processors: New way to achieve high system performance. In: International symposium on parallel computing in electrical engineering (PARELEC’06). Bialystok, Poland, pp 9–13. https://doi.org/10.1109/PARELEC.2006.54 – reference: Tsapanos N, Tefas A, Nikolaidis N, Pitas I (2016) Efficient mapreduce kernel k-means for big data clustering. In: Proceedings of the 9th hellenic conference on artificial intelligence, SETN ’16. ACM, Thessaloniki, pp 28:1–28:5. https://doi.org/10.1145/2903220.2903255 – reference: AkhterSRobertsJMulti-core programming: increasing performance through software multi-threading20061HillsboroIntel Press – reference: NanniMPedreschiDTime-focused clustering of trajectories of moving objectsJ Intell Inf Syst200627326728910.1007/s10844-006-9953-7 – reference: JinRKouCLiuRLiYEfficient parallel spectral clustering algorithm design for large data sets under cloud computing environmentJ Cloud Comput Adv Syst Appl2013211810.1186/2192-113X-2-18 – reference: Jia F, Wang C, Li X, Zhou X (2015) SAKMA: specialized FPGA-based accelerator architecture for data-intensive k-means algorithms. In: Algorithms and architectures for parallel processing. Springer, Cham, pp 106–119. https://doi.org/10.1007/978-3-319-27122-4_8 – reference: Winterstein F, Bayliss S, Constantinides GA (2013) FPGA-based k-means clustering using tree-based data structures. In: The 23rd international conference on field programmable logic and applications. IEEE, Porto, pp 1–6. https://doi.org/10.1109/FPL.2013.6645501 – reference: Kriegel HP, Pfeifle M (2005) Density-based clustering of uncertain data. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining. ACM, Chicago, pp 672–677. https://doi.org/10.1145/1081870.1081955 – reference: SotiropoulouCLGkaitatzisSAnnoviABerettaMGiannettiPKordasKLucianoPNikolaidisSPetridouCVolpiGA multi-core FPGA-based 2D-clustering implementation for real-time image processingIEEE Trans Nuclear Sci20146163599360610.1109/TNS.2014.2364183 – reference: Li C, Zhang Y, Jiao M, Yu G (2014) Mux-Kmeans: multiplex Kmeans for clustering large-scale data set. In: Proceedings of the 5th ACM workshop on scientific cloud computing, ScienceCloud ’14. ACM, Vancouver, pp 25–32. https://doi.org/10.1145/2608029.2608033 – reference: ShiSYueQWangQFPGA based accelerator for parallel DBSCAN algorithmComput Model New Technol2014182135142 – reference: Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX conference on hot topics in cloud computing, HotCloud’10. USENIX Association, Berkeley – reference: ValiantLGA bridging model for parallel computationCommun ACM199033810311110.1145/79173.79181 – reference: YanWBrahmakshatriyaUXueYGilderMWiseBp-PIC: parallel power iteration clustering for big dataJ Parallel Distrib Comput201373335235910.1016/j.jpdc.2012.06.009 – reference: DengZHuYZhuMHuangXDuBA scalable and fast optics for clustering trajectory big dataCluster Comput201518254956210.1007/s10586-014-0413-9 – volume: 71 start-page: 955 issue: 7 year: 2011 ident: 9918_CR79 publication-title: J Parallel Distrib Comput doi: 10.1016/j.jpdc.2011.03.007 – volume: 93 start-page: 78 issue: Supplement C year: 2017 ident: 9918_CR8 publication-title: Pattern Recognit Lett doi: 10.1016/j.patrec.2016.07.027 – volume-title: Multi-core programming: increasing performance through software multi-threading year: 2006 ident: 9918_CR2 – volume: 13 start-page: 197 issue: 2 year: 2005 ident: 9918_CR73 publication-title: J Netw Syst Manag doi: 10.1007/s10922-005-4441-x – volume: 12 start-page: 66 issue: 3 year: 2010 ident: 9918_CR67 publication-title: Comput Sci Eng doi: 10.1109/MCSE.2010.69 – volume: 52 start-page: 505 issue: 8 year: 2006 ident: 9918_CR14 publication-title: J Syst Archit doi: 10.1016/j.sysarc.2006.02.003 – volume: E95–D start-page: 2327 issue: 9 year: 2012 ident: 9918_CR4 publication-title: IEICE Trans Inf Syst doi: 10.1587/transinf.E95.D.2327 – ident: 9918_CR6 doi: 10.1145/304182.304187 – volume: 107 start-page: 442 issue: Supplement C year: 2017 ident: 9918_CR48 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2017.03.138 – ident: 9918_CR59 doi: 10.1109/WETICE.2016.26 – ident: 9918_CR3 doi: 10.1145/2487575.2487628 – volume: 18 start-page: 539 issue: 3 year: 2014 ident: 9918_CR46 publication-title: Soft Comput doi: 10.1007/s00500-013-1074-y – volume: 96 start-page: 879 issue: 5 year: 2008 ident: 9918_CR56 publication-title: Proc IEEE doi: 10.1109/JPROC.2008.917757 – volume: 2 start-page: 18 issue: 1 year: 2013 ident: 9918_CR39 publication-title: J Cloud Comput Adv Syst Appl doi: 10.1186/2192-113X-2-18 – ident: 9918_CR17 – volume: 24 start-page: 881 issue: 7 year: 2002 ident: 9918_CR40 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2002.1017616 – year: 2017 ident: 9918_CR16 publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2017.12.002 – ident: 9918_CR80 – ident: 9918_CR44 doi: 10.6028/jres.045.026 – volume: 11 start-page: 169 issue: 1 year: 2010 ident: 9918_CR42 publication-title: BMC Bioinform doi: 10.1186/1471-2105-11-169 – volume: 18 start-page: 369 issue: Supplement C year: 2013 ident: 9918_CR5 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2013.05.200 – volume: 2 start-page: 339 issue: 4 year: 2016 ident: 9918_CR10 publication-title: IEEE Trans Big Data doi: 10.1109/TBDATA.2016.2622288 – volume: 18 start-page: 135 issue: 2 year: 2014 ident: 9918_CR62 publication-title: Comput Model New Technol – volume: 22 start-page: 143 year: 2014 ident: 9918_CR21 publication-title: Turk J Electr Eng Comput Sci doi: 10.3906/elk-1202-83 – ident: 9918_CR70 doi: 10.1145/2903220.2903255 – ident: 9918_CR50 doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.85 – volume: 9 start-page: 2:1 issue: 1 year: 2015 ident: 9918_CR60 publication-title: ACM Trans Reconfig Technol Syst doi: 10.1145/2724722 – ident: 9918_CR49 doi: 10.1145/2608029.2608033 – ident: 9918_CR47 – ident: 9918_CR22 – volume: 30 start-page: 121 issue: 1 year: 2008 ident: 9918_CR72 publication-title: SIAM J Matrix Anal Appl doi: 10.1137/040608635 – ident: 9918_CR27 doi: 10.1109/PARELEC.2006.54 – ident: 9918_CR26 doi: 10.1109/IPDPSW.2010.5470922 – ident: 9918_CR24 doi: 10.1007/978-1-4614-3594-5_2 – ident: 9918_CR38 doi: 10.1007/978-3-319-27122-4_8 – ident: 9918_CR1 doi: 10.1016/B978-012722442-8/50016-1 – volume: 8 start-page: 83 issue: 1 year: 2014 ident: 9918_CR35 publication-title: Front Comput Sci doi: 10.1007/s11704-013-3158-3 – volume: 20 start-page: 359 issue: 1 year: 1998 ident: 9918_CR41 publication-title: SIAM J Sci Comput doi: 10.1137/S1064827595287997 – ident: 9918_CR37 doi: 10.1109/AHS.2011.5963944 – ident: 9918_CR13 doi: 10.1109/HPCSim.2015.7237086 – volume: 2 start-page: 267 issue: 3 year: 2014 ident: 9918_CR23 publication-title: IEEE Trans Emerg Top Comput doi: 10.1109/TETC.2014.2330519 – volume: 6 start-page: 281 issue: 3–4 year: 1999 ident: 9918_CR9 publication-title: J Comput Biol doi: 10.1089/106652799318274 – ident: 9918_CR25 doi: 10.1145/2020408.2020516 – ident: 9918_CR55 doi: 10.1109/HOTCHIPS.2008.7476525 – ident: 9918_CR57 doi: 10.1145/2503210.2503255 – volume: 61 start-page: 3599 issue: 6 year: 2014 ident: 9918_CR66 publication-title: IEEE Trans Nuclear Sci doi: 10.1109/TNS.2014.2364183 – volume: 18 start-page: 549 issue: 2 year: 2015 ident: 9918_CR18 publication-title: Cluster Comput doi: 10.1007/s10586-014-0413-9 – volume: 73 start-page: 352 issue: 3 year: 2013 ident: 9918_CR77 publication-title: J Parallel Distrib Comput doi: 10.1016/j.jpdc.2012.06.009 – ident: 9918_CR19 doi: 10.1145/1851476.1851593 – volume: 69 start-page: 845 issue: 2 year: 2014 ident: 9918_CR29 publication-title: J Supercomput doi: 10.1007/s11227-014-1185-y – volume: 70 start-page: 1249 issue: 3 year: 2014 ident: 9918_CR15 publication-title: J Supercomput doi: 10.1007/s11227-014-1225-7 – ident: 9918_CR30 doi: 10.1109/IPDPSW.2016.57 – volume: 8 start-page: 289 issue: 3–4 year: 2000 ident: 9918_CR61 publication-title: VLDB J Int J Very Large Data Bases doi: 10.1007/s007780050009 – volume: 9 start-page: 679 issue: 3 year: 2012 ident: 9918_CR12 publication-title: IEEE/ACM Trans Comput Biol Bioinform doi: 10.1109/TCBB.2011.68 – ident: 9918_CR31 doi: 10.1016/B978-0-12-381479-1.00010-1 – volume: 30 start-page: 1575 issue: 7 year: 2002 ident: 9918_CR20 publication-title: Nucleic Acids Res doi: 10.1093/nar/30.7.1575 – ident: 9918_CR53 – volume: 80 start-page: 951 year: 2016 ident: 9918_CR52 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2016.05.389 – volume: 2 start-page: 8 issue: 1 year: 2014 ident: 9918_CR63 publication-title: J Big Data doi: 10.1186/s40537-014-0008-6 – ident: 9918_CR28 doi: 10.1145/2851613.2851643 – volume: 20 start-page: 1130 issue: 6 year: 2012 ident: 9918_CR34 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2012.2201485 – volume: 69 start-page: 452 issue: 1 year: 2014 ident: 9918_CR68 publication-title: J Supercomput doi: 10.1007/s11227-014-1174-1 – ident: 9918_CR76 doi: 10.1109/FPL.2013.6645501 – volume-title: Field-programmable gate arrays year: 1992 ident: 9918_CR11 doi: 10.1007/978-1-4615-3572-0 – volume: 51 start-page: 147 issue: Supplement C year: 2017 ident: 9918_CR7 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2016.11.045 – volume: 27 start-page: 267 issue: 3 year: 2006 ident: 9918_CR54 publication-title: J Intell Inf Syst doi: 10.1007/s10844-006-9953-7 – volume: 7 start-page: 26 issue: 4 year: 1999 ident: 9918_CR65 publication-title: IEEE Concurr doi: 10.1109/4434.806976 – volume: 28 start-page: 100 issue: 1 year: 1979 ident: 9918_CR33 publication-title: Appl Stat doi: 10.2307/2346830 – ident: 9918_CR74 doi: 10.1109/CBD.2016.016 – ident: 9918_CR36 doi: 10.1145/2806416.2806545 – ident: 9918_CR75 doi: 10.1109/ICCT.2012.6511380 – ident: 9918_CR45 – ident: 9918_CR43 doi: 10.1145/1081870.1081955 – ident: 9918_CR32 doi: 10.1007/978-3-540-77220-0_21 – ident: 9918_CR64 doi: 10.1109/ICACCI.2016.7732323 – ident: 9918_CR78 doi: 10.1109/SMC.2013.480 – ident: 9918_CR83 doi: 10.1007/978-3-642-10665-1_71 – volume: 48 start-page: 2685 issue: 8 year: 2015 ident: 9918_CR69 publication-title: Pattern Recognit doi: 10.1016/j.patcog.2015.02.020 – volume: 33 start-page: 103 issue: 8 year: 1990 ident: 9918_CR71 publication-title: Commun ACM doi: 10.1145/79173.79181 – ident: 9918_CR81 doi: 10.1109/BigData.2016.7840708 – volume: 344 start-page: 1492 issue: 6191 year: 2014 ident: 9918_CR58 publication-title: Science doi: 10.1126/science.1242072 – ident: 9918_CR51 doi: 10.1007/978-3-319-48472-3_20 – ident: 9918_CR82 doi: 10.1109/IPDPS.2010.5470429 |
| SSID | ssj0005243 |
| Score | 2.4964182 |
| Snippet | Data clustering is one of the most studied data mining tasks. It aims, through various methods, to discover previously unknown groups within the data sets. In... |
| SourceID | proquest gale crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 2411 |
| SubjectTerms | Algorithms Artificial Intelligence Big Data Classification Clustering Computer Science Context Data mining Digital integrated circuits Field programmable gate arrays Information retrieval Microprocessors Peer relationships Peer to peer computing Peers Social networks Surveys |
| SummonAdditionalLinks | – databaseName: ABI/INFORM Global dbid: M0C link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07T8MwED5BYWDhjSgU5AGJASyal-NMqBQQC4gBpG6W49gFKbSlSZH495xTh_IQXZiTOE4-39139j0AjjLfMCODiPpSa4oM3CYrZylVWralySLd5mnVbCK-u-O9XnLvNtwKF1ZZ68RKUWdDZffIz9BUx6hsURzOR6_Udo2yp6uuhcYiLFlmY0P6btvdLyEe06g5nyUUXQvPJc241LmQ-dQ6T8iRPFwt3wzTT_X865y0Mj_Xa_-d-DqsOuJJOtOVsgELerAJa3VTB-JkfAviDikm4zf9ToYDYguD57nOicontqICTo3IvI_Dl08vBUHCSy6e--RSlnIbHq-vHro31HVXoCqIeEk9GcqYy4iptgnSQKKrmLDEZCpWgTZ-ihdTY_kLZxlLGQ8Y4yZkBhkX4o6fswONwXCgd4FEaaw8pI5BrL1QmTQxCpkE12HGmAwNa4JX_1qhXOlx2wEjF7OiyRYOgXCICg7hN-Hk85nRtPDG3LuPLWLCSiWOrKRLLsD52fpWooMm2EdfkntNaNUwCSeuhZhh1ITTGujZ5b_fuzd_tH1Y8W0MTBXp04JGOZ7oA1hWb-VzMT6sFusHrq_s3w priority: 102 providerName: ProQuest |
| Title | A survey on parallel clustering algorithms for Big Data |
| URI | https://link.springer.com/article/10.1007/s10462-020-09918-2 https://www.proquest.com/docview/2507362763 |
| Volume | 54 |
| WOSCitedRecordID | wos000575762200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-7462 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005243 issn: 0269-2821 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1JT-MwFH5imcNc2GZGlKXyAYkDY6nZbOdYNiEhSsUOF8tx7FIptKhJkfj3PKcJ2wASc7EU2fHynu33PfktABupb5lVQUR9ZQxFBO6cldOEaqNayqaRaYmkTDbBOx1xdRV3K6ewvLZ2r58ky5v6lbNbyHzq1B1ENR7ydxpmUdwJl7Dh5PTilWHHxFbOZzFFhcKrXGU-7uONOHp_Kf_zOloKnf35_5vuAsxVIJO0J7tiEabMYAnm6wQOpDrPv4C3ST4ePZhHMhwQFwQ8y0xGdDZ20RNwMKKy3nDUL27vcoLglmz3e2RXFeo3nO_vne0c0CqTAtVBJArqqVBxoSKmWzZIAoVqYcxim2quA2P9BCsT67CKYClLmAgYEzZkFtEV8hivoD8wMxgOzDKQKOHaQ5gYcOOF2iax1YgahAlTxlRoWQO8mqBSV2HGXbaLTL4ESHaUkUgZWVJG-g3Yev7nfhJk48vWm45P0p1A7FmrypEA5-diWck2ilsf9UbhNWCtZqWsjmYuEfNxlNq4qAb8rVn3Uv35uCvfa74KP31n_1Ja-azBTDEam3X4oR-Kfj5qwjS_vG7C7PZep3uCX4ecYnnU2nGld4xlN7pplhv7Cals6UU |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NT9swFH9ibNK4wGCbKAPmw9AOm7XGSWzngFD5Eqis2oFJ3DzHsQEptNCkRf2n9jfynCaUMY0bh52T-Ot9_V78PgA-Zcxxp8OYMm0tRQTuk5WzlBqr29plsW3LtGo2IXo9eXaW_JiD300ujA-rbHRipaizgfH_yL-hqRaobFEcdq5vqO8a5W9XmxYaU7bo2sktumzF9vE-0neLscOD070jWncVoCaMZUkDHWkhdcxN24VpqNFFSnjiMiNMaB1L8WHqvN2WPOMplyHn0kXcIdLA_eL8OO4LeBmFUni56gr6IKRkGqXHeELRlQnqJJ06VS_ijHpnDTFZgNz5hyF8bA7-upetzN3h0v92UG9gsQbWpDOVhGWYs_0VWGqaVpBah70F0SHFaDi2EzLoE1_4PM9tTkw-8hUj8CiIzs9xO-XFVUEQ0JPdy3Oyr0v9Dn4-y_Lfw3x_0LerQOJUmAChcShsEBmXJs4gUpI2yjjXkeMtCBpSKlOXVvcdPnI1Kwrtya-Q_Koiv2It-HL_zfW0sMiTb3_2HKK81sGRja6TJ3B9vn6X6iDEYOgry6AF6w1bqFodFWrGEy342jDW7PG_5117erSP8Pro9PuJOjnudT_AAvPxPlVU0zrMl8OR3YBXZlxeFsPNSlAI_HpuhrsDLPxKBQ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB5RQBUXXi3q8mh9oOLQWmycxHEOFdp2WYGoVnsoEurFdRybIoVd2GRB_DV-XcdZh22pyo1Dz04cPz7PfBPPA2A3Z5ZbFcaUKWMoMnAXrJxnVBvVVjaPTVtkdbGJpN8XZ2fpYA7um1gY51bZyMRaUOcj7f6R76OqTlDY4nHYt94tYtDtHVxdU1dByt20NuU0phA5MXe3aL6Vn467uNfvGesdfvtyRH2FAarDWFQ0UJFKhIq5btswCxWaSylPba4THRrLMmzMrNPhguc84yLkXNiIW2QdOHccC_b7AhYStDGdO-Eg_v6be8nUY4_xlKJZE_iAHR-2F3FGneGG_CxApP6hFB-rhr_uaGvV11v5nxdtFZY94Sad6QlZgzkzXIeVppgF8bLtFSQdUk7GN-aOjIbEJUQvClMQXUxcJglcFqKKc5xO9fOyJEj0yeeLc9JVlXoNp88y_A2YH46G5g2QOEt0gJQ5TEwQaZulViODEibKOVeR5S0Imm2V2qdcd5U_CjlLFu2gIBEKsoaCZC348PDO1TThyJNP7zm0SCeNsGetfFAFjs_l9ZIdpB4MbWgRtGC7gYj0YqqUM3y04GMDslnzv7-7-XRv7-Al4kx-Pe6fbMESc25AtbPTNsxX44nZgUV9U12U47f1mSHw47nx9guuVVMp |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+survey+on+parallel+clustering+algorithms+for+Big+Data&rft.jtitle=The+Artificial+intelligence+review&rft.au=Dafir%2C+Zineb&rft.au=Lamari%2C+Yasmine&rft.au=Slaoui%2C+Said+Chah&rft.date=2021-04-01&rft.issn=0269-2821&rft.eissn=1573-7462&rft.volume=54&rft.issue=4&rft.spage=2411&rft.epage=2443&rft_id=info:doi/10.1007%2Fs10462-020-09918-2&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10462_020_09918_2 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0269-2821&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0269-2821&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0269-2821&client=summon |