Relative density-based clustering algorithm for identifying diverse density clusters effectively
Clustering is an important part of data mining. The existing clustering algorithm failed in the data set with uneven density distribution. In this paper, we propose a novel clustering algorithm relative density-based clustering algorithm for identifying diverse density clusters effectively called ID...
Saved in:
| Published in: | Neural computing & applications Vol. 33; no. 16; pp. 10141 - 10157 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Springer London
01.08.2021
Springer Nature B.V |
| Subjects: | |
| ISSN: | 0941-0643, 1433-3058 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Clustering is an important part of data mining. The existing clustering algorithm failed in the data set with uneven density distribution. In this paper, we propose a novel clustering algorithm relative density-based clustering algorithm for identifying diverse density clusters effectively called IDDC. It can effectively identify clusters in data sets with different densities and can also handle outliers. We first compute relative density for each data point. Then, the density peak points are screened and the initial clusters are obtained according to these peak points. The strategy for assigning the remaining points is to find unallocated points from the perspective of the cluster, which can effectively identify different density. In experiments, we compare the proposed algorithm IDDC with some existing algorithms on synthetic and real-world data sets. The results show that IDDC performs better than those existing algorithms, especially clustering on data set with uneven density distribution. |
|---|---|
| AbstractList | Clustering is an important part of data mining. The existing clustering algorithm failed in the data set with uneven density distribution. In this paper, we propose a novel clustering algorithm relative density-based clustering algorithm for identifying diverse density clusters effectively called IDDC. It can effectively identify clusters in data sets with different densities and can also handle outliers. We first compute relative density for each data point. Then, the density peak points are screened and the initial clusters are obtained according to these peak points. The strategy for assigning the remaining points is to find unallocated points from the perspective of the cluster, which can effectively identify different density. In experiments, we compare the proposed algorithm IDDC with some existing algorithms on synthetic and real-world data sets. The results show that IDDC performs better than those existing algorithms, especially clustering on data set with uneven density distribution. |
| Author | Yang, Youlong Wang, Yuying |
| Author_xml | – sequence: 1 givenname: Yuying orcidid: 0000-0001-8325-1814 surname: Wang fullname: Wang, Yuying email: wangyuying@stu.xidian.edu.cn organization: School of Mathematics and Statistics, Xidian University – sequence: 2 givenname: Youlong surname: Yang fullname: Yang, Youlong organization: School of Mathematics and Statistics, Xidian University |
| BookMark | eNp9kE1LAzEQhoNUsFX_gKcFz6uTr93sUYpfUBBEzzHdndSU7W5NUmH_vVnqB3hoYMhh3meSeWZk0vUdEnJB4YoClNcBQDKaw1iyLMucHZEpFZznHKSakClUIrUKwU_ILIQ1AIhCySl5e8bWRPeJWYNdcHHIlyZgk9XtLkT0rltlpl313sX3TWZ7n7mUi84OY6dJnA-_6A8UMrQW63FqO5yRY2vagOff9yl5vbt9mT_ki6f7x_nNIq85rWIusUKrjJDAEItSFVJRMKKu02JIqW3QCG4bng4qIynllC0LTBspVamK8lNyuZ-79f3HDkPU637nu_SkZlJUQjFgZUqxfar2fQgerd56tzF-0BT0aFLvTWoYazSpWYLUP6h2MUnru-iNaw-jfI-G7egS_d-vDlBfDuOLPA |
| CitedBy_id | crossref_primary_10_1016_j_ins_2022_11_091 crossref_primary_10_1007_s00357_024_09475_1 crossref_primary_10_3389_fams_2025_1598165 crossref_primary_10_1016_j_measurement_2025_118869 crossref_primary_10_1016_j_neucom_2024_127329 crossref_primary_10_1016_j_eswa_2021_116143 crossref_primary_10_3389_fphy_2025_1623161 crossref_primary_10_1016_j_ins_2024_120380 crossref_primary_10_7717_peerj_cs_1921 crossref_primary_10_1155_2021_1336900 crossref_primary_10_1016_j_engappai_2024_108883 crossref_primary_10_1007_s10115_023_02038_7 crossref_primary_10_1016_j_eswa_2023_121860 crossref_primary_10_1016_j_neucom_2025_131435 crossref_primary_10_1109_ACCESS_2023_3329429 crossref_primary_10_1109_ACCESS_2025_3563990 |
| Cites_doi | 10.1016/j.ins.2018.03.031 10.1109/TPAMI.2002.1033218 10.1109/T-C.1971.223083 10.1007/s00357-006-0017-z 10.1016/j.knosys.2016.02.001 10.1186/1471-2105-8-3 10.1016/j.patrec.2009.09.011 10.1016/j.ins.2016.08.009 10.1016/j.knosys.2018.06.021 10.1007/11590316_1 10.1016/j.patcog.2007.04.010 10.1016/j.ins.2016.03.011 10.1109/TNN.2005.845141 10.1016/j.knosys.2019.105454. 10.1109/TPAMI.2010.231 10.4249/scholarpedia.1883 10.1016/j.ejor.2006.09.023 10.1109/TNNLS.2013.2293795 10.1145/1217299.1217303 10.1007/BF01908601 10.1126/science.1242072 10.1016/j.knosys.2017.11.025 10.1007/s00778-010-0189-3 10.1109/MDM.2008.17 10.1002/9780470050118 10.1007/s10489-018-1238-7 10.1007/978-3-642-37401-2_78 10.1145/342009.335388 10.1016/j.patrec.2016.05.007 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 – notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021. |
| DBID | AAYXX CITATION 8FE 8FG AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS |
| DOI | 10.1007/s00521-021-05777-2 |
| DatabaseName | CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland ProQuest SciTech Premium Collection Technology Collection Advanced Technologies & Aerospace Collection ProQuest Central ProQuest Technology Collection ProQuest One ProQuest Central SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Proquest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) 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 |
| DatabaseTitle | CrossRef Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Advanced Technologies & Aerospace Collection |
| Database_xml | – sequence: 1 dbid: P5Z name: Advanced Technologies & Aerospace Database url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1433-3058 |
| EndPage | 10157 |
| ExternalDocumentID | 10_1007_s00521_021_05777_2 |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61573266 funderid: http://dx.doi.org/10.13039/501100001809 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB DWQXO PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c319t-5e9ef8a4502ee67865810a4cc005e11fdea43fd3333e8a511312b6e0648898913 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 17 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000628486900003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0941-0643 |
| IngestDate | Wed Nov 05 00:42:59 EST 2025 Sat Nov 29 02:59:20 EST 2025 Tue Nov 18 21:31:35 EST 2025 Fri Feb 21 02:48:20 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 16 |
| Keywords | Allocation strategy nearest neighbors Relative density Density-based clustering |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c319t-5e9ef8a4502ee67865810a4cc005e11fdea43fd3333e8a511312b6e0648898913 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-8325-1814 |
| PQID | 2549482027 |
| PQPubID | 2043988 |
| PageCount | 17 |
| ParticipantIDs | proquest_journals_2549482027 crossref_primary_10_1007_s00521_021_05777_2 crossref_citationtrail_10_1007_s00521_021_05777_2 springer_journals_10_1007_s00521_021_05777_2 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-08-01 |
| PublicationDateYYYYMMDD | 2021-08-01 |
| PublicationDate_xml | – month: 08 year: 2021 text: 2021-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: Heidelberg |
| PublicationTitle | Neural computing & applications |
| PublicationTitleAbbrev | Neural Comput & Applic |
| PublicationYear | 2021 |
| Publisher | Springer London Springer Nature B.V |
| Publisher_xml | – name: Springer London – name: Springer Nature B.V |
| References | Cheng, Zhang, Huang (CR7) 2020; 193 Huang, Ye, Zhang (CR19) 2014; 25 Baulieu (CR3) 1989; 6 Cai, He, Han, Huang (CR6) 2011; 33 CR39 Rodriguez, Laio (CR29) 2014; 344 CR14 CR12 CR11 CR10 CR32 Jain (CR20) 2010; 31 Albatineh, Niewiadomska-Bugaj, Mihalko (CR1) 2006; 23 Liu, Wang, Yu (CR23) 2018; 450 Peterson (CR27) 2009; 4 Bartel, Mucha, Dolata (CR2) 2003; 48 Hong, Yeung (CR18) 2008; 41 Jain, Law (CR21) 2005; 3776 Gionis, Mannila, Tsaparas (CR15) 2007; 1 Du, Ding, Jia (CR9) 2016; 99 Zahn, Zahn (CR37) 1971; 20 Xiao, Zhou, Zhang, Hui, Yang (CR33) 2016; 44 Xie, Gao, Xie (CR35) 2016; 354 CR4 Zhou, Si, Zhang, Zheng (CR38) 2018; 159 Olafsson, Li, Wu (CR26) 2008; 187 Xie, Xiong, Zhang, Feng, Ma (CR34) 2018; 142 CR28 CR25 CR24 Chen, Tang, Zhou, Wang, Du, Wang, Pei (CR36) 2018; 433 CR22 Veenman, Reinders, Backer (CR31) 2002; 24 Fu, Medico (CR13) 2007; 8 Friedman, Hastie, Tibshirani (CR5) 2009 Han, Kamber, Jian (CR16) 2011; 5 He, Wu, Cai (CR17) 2007; 24 Rui, Wunsch (CR30) 2005; 16 Deng, He, Han (CR8) 2011; 20 HG Bartel (5777_CR2) 2003; 48 A Gionis (5777_CR15) 2007; 1 J Han (5777_CR16) 2011; 5 5777_CR39 5777_CR4 Y Chen (5777_CR36) 2018; 433 D Cai (5777_CR6) 2011; 33 M Du (5777_CR9) 2016; 99 R Liu (5777_CR23) 2018; 450 D Cheng (5777_CR7) 2020; 193 AK Jain (5777_CR21) 2005; 3776 L He (5777_CR17) 2007; 24 Z Zhou (5777_CR38) 2018; 159 AN Albatineh (5777_CR1) 2006; 23 C Deng (5777_CR8) 2011; 20 CJ Veenman (5777_CR31) 2002; 24 A Rodriguez (5777_CR29) 2014; 344 Xu Rui (5777_CR30) 2005; 16 5777_CR14 L Xiao (5777_CR33) 2016; 44 5777_CR12 5777_CR11 5777_CR10 5777_CR32 L Fu (5777_CR13) 2007; 8 J Friedman (5777_CR5) 2009 5777_CR28 S Olafsson (5777_CR26) 2008; 187 FB Baulieu (5777_CR3) 1989; 6 CT Zahn (5777_CR37) 1971; 20 C Hong (5777_CR18) 2008; 41 AK Jain (5777_CR20) 2010; 31 X Huang (5777_CR19) 2014; 25 LE Peterson (5777_CR27) 2009; 4 J Xie (5777_CR34) 2018; 142 J Xie (5777_CR35) 2016; 354 5777_CR25 5777_CR24 5777_CR22 |
| References_xml | – ident: CR22 – volume: 450 start-page: 200 year: 2018 end-page: 226 ident: CR23 article-title: Shared-nearest-neighbor-based clustering by fast search and find of density peaks publication-title: Inf Sci doi: 10.1016/j.ins.2018.03.031 – volume: 44 start-page: 23 issue: 12 year: 2016 end-page: 28 ident: CR33 article-title: Study of reactive power control partitioning method with spectral cluster analysis based on PCA publication-title: Shaanxi Electr Power – volume: 24 start-page: 1273 issue: 9 year: 2002 end-page: 1280 ident: CR31 article-title: A maximum variance cluster algorithm publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2002.1033218 – volume: 20 start-page: 68 issue: 1 year: 1971 end-page: 86 ident: CR37 article-title: Graph-theoretical methods for detecting and describing gestalt clusters publication-title: IEEE Trans Comput doi: 10.1109/T-C.1971.223083 – volume: 23 start-page: 301 issue: 2 year: 2006 end-page: 313 ident: CR1 article-title: On similarity indices and correction for chance agreement publication-title: J Classif doi: 10.1007/s00357-006-0017-z – ident: CR4 – ident: CR14 – ident: CR39 – volume: 99 start-page: 135 issue: may1 year: 2016 end-page: 145 ident: CR9 article-title: Study on density peaks clustering based on k-nearest neighbors and principal component analysis publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2016.02.001 – volume: 8 start-page: 1 year: 2007 end-page: 15 ident: CR13 article-title: Flame, a novel fuzzy clustering method for the analysis of DNA microarray data publication-title: BMC Bioinform doi: 10.1186/1471-2105-8-3 – volume: 31 start-page: 651 issue: 8 year: 2010 end-page: 666 ident: CR20 article-title: Data clustering: 50 years beyond k-means publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2009.09.011 – volume: 5 start-page: 1 issue: 4 year: 2011 end-page: 18 ident: CR16 article-title: Data mining: concepts and techniques: concepts and techniques publication-title: Data Min Concepts Models Methods Algorithms Second Ed – volume: 433 start-page: 510 year: 2018 end-page: 26 ident: CR36 article-title: Decentralized clustering by finding loose and distributed density cores publication-title: Inf Sci doi: 10.1016/j.ins.2016.08.009 – ident: CR12 – volume: 159 start-page: 309 year: 2018 end-page: 320 ident: CR38 article-title: Robust clustering by identifying the veins of clusters based on kernel density estimation publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2018.06.021 – volume: 3776 start-page: 1 year: 2005 end-page: 10 ident: CR21 article-title: Data clustering: a user’s dilemma publication-title: Lect Notes Comput Sci doi: 10.1007/11590316_1 – ident: CR10 – volume: 41 start-page: 191 issue: 1 year: 2008 end-page: 203 ident: CR18 article-title: Robust path-based spectral clustering publication-title: Pattern Recogn doi: 10.1016/j.patcog.2007.04.010 – volume: 354 start-page: 19 year: 2016 end-page: 40 ident: CR35 article-title: Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors publication-title: Inf Sci doi: 10.1016/j.ins.2016.03.011 – volume: 16 start-page: 645 issue: 3 year: 2005 end-page: 678 ident: CR30 article-title: Survey of clustering algorithms publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2005.845141 – volume: 48 start-page: 1070 issue: 48 year: 2003 end-page: 1070 ident: CR2 article-title: On a modification of a graph theory based partitioning method in cluster analysis publication-title: Match Commun Math Comput Chem – volume: 193 start-page: 105454 year: 2020 ident: CR7 article-title: Dense members of local cores-based density peaks clustering algorithm publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2019.105454. – ident: CR25 – volume: 33 start-page: 1548 issue: 8 year: 2011 end-page: 1560 ident: CR6 article-title: Graph regularized non-negative matrix factorization for data representation publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2010.231 – volume: 4 start-page: 1883 issue: 2 year: 2009 ident: CR27 article-title: K-nearest neighbor publication-title: Scholarpedia doi: 10.4249/scholarpedia.1883 – volume: 187 start-page: 1429 issue: 3 year: 2008 end-page: 1448 ident: CR26 article-title: Operations research and data mining publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2006.09.023 – year: 2009 ident: CR5 publication-title: The elements of statistical learning – volume: 24 start-page: 10 issue: 1 year: 2007 end-page: 13 ident: CR17 article-title: Survey of clustering algorithms in data mining publication-title: Appl Res Comput – ident: CR11 – volume: 25 start-page: 1433 issue: 8 year: 2014 end-page: 1446 ident: CR19 article-title: Extensions of kmeans-type algorithms: a new clustering framework by integrating intracluster compactness and intercluster separation publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2013.2293795 – volume: 1 start-page: 4-es issue: 1 year: 2007 ident: CR15 article-title: Clustering aggregation publication-title: ACM Trans Knowl Discov Data doi: 10.1145/1217299.1217303 – volume: 6 start-page: 233 issue: 1 year: 1989 end-page: 246 ident: CR3 article-title: A classification of presence/absence based dissimilarity coefficients publication-title: J Classif doi: 10.1007/BF01908601 – volume: 344 start-page: 1492 issue: 6191 year: 2014 end-page: 1496 ident: CR29 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – ident: CR32 – volume: 142 start-page: 58 year: 2018 end-page: 70 ident: CR34 article-title: Density core-based clustering algorithm with dynamic scanning radius publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2017.11.025 – ident: CR28 – ident: CR24 – volume: 20 start-page: 21 issue: 1 year: 2011 end-page: 33 ident: CR8 article-title: Speed up kernel discriminant analysis publication-title: Vldb J doi: 10.1007/s00778-010-0189-3 – ident: 5777_CR14 doi: 10.1109/MDM.2008.17 – volume: 159 start-page: 309 year: 2018 ident: 5777_CR38 publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2018.06.021 – volume: 3776 start-page: 1 year: 2005 ident: 5777_CR21 publication-title: Lect Notes Comput Sci doi: 10.1007/11590316_1 – volume: 44 start-page: 23 issue: 12 year: 2016 ident: 5777_CR33 publication-title: Shaanxi Electr Power – ident: 5777_CR32 doi: 10.1002/9780470050118 – ident: 5777_CR25 – volume: 354 start-page: 19 year: 2016 ident: 5777_CR35 publication-title: Inf Sci doi: 10.1016/j.ins.2016.03.011 – ident: 5777_CR12 doi: 10.1007/s10489-018-1238-7 – volume: 1 start-page: 4-es issue: 1 year: 2007 ident: 5777_CR15 publication-title: ACM Trans Knowl Discov Data doi: 10.1145/1217299.1217303 – volume: 4 start-page: 1883 issue: 2 year: 2009 ident: 5777_CR27 publication-title: Scholarpedia doi: 10.4249/scholarpedia.1883 – volume: 16 start-page: 645 issue: 3 year: 2005 ident: 5777_CR30 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2005.845141 – volume: 142 start-page: 58 year: 2018 ident: 5777_CR34 publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2017.11.025 – volume: 23 start-page: 301 issue: 2 year: 2006 ident: 5777_CR1 publication-title: J Classif doi: 10.1007/s00357-006-0017-z – volume: 20 start-page: 68 issue: 1 year: 1971 ident: 5777_CR37 publication-title: IEEE Trans Comput doi: 10.1109/T-C.1971.223083 – volume: 344 start-page: 1492 issue: 6191 year: 2014 ident: 5777_CR29 publication-title: Science doi: 10.1126/science.1242072 – volume: 25 start-page: 1433 issue: 8 year: 2014 ident: 5777_CR19 publication-title: IEEE Trans Neural Netw Learn Syst doi: 10.1109/TNNLS.2013.2293795 – volume: 99 start-page: 135 issue: may1 year: 2016 ident: 5777_CR9 publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2016.02.001 – volume: 5 start-page: 1 issue: 4 year: 2011 ident: 5777_CR16 publication-title: Data Min Concepts Models Methods Algorithms Second Ed – volume: 41 start-page: 191 issue: 1 year: 2008 ident: 5777_CR18 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2007.04.010 – ident: 5777_CR10 – volume: 193 start-page: 105454 year: 2020 ident: 5777_CR7 publication-title: Knowl Based Syst doi: 10.1016/j.knosys.2019.105454. – volume: 8 start-page: 1 year: 2007 ident: 5777_CR13 publication-title: BMC Bioinform doi: 10.1186/1471-2105-8-3 – ident: 5777_CR28 – volume: 187 start-page: 1429 issue: 3 year: 2008 ident: 5777_CR26 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2006.09.023 – volume: 33 start-page: 1548 issue: 8 year: 2011 ident: 5777_CR6 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2010.231 – ident: 5777_CR22 – volume-title: The elements of statistical learning year: 2009 ident: 5777_CR5 – ident: 5777_CR24 doi: 10.1007/978-3-642-37401-2_78 – volume: 48 start-page: 1070 issue: 48 year: 2003 ident: 5777_CR2 publication-title: Match Commun Math Comput Chem – volume: 433 start-page: 510 year: 2018 ident: 5777_CR36 publication-title: Inf Sci doi: 10.1016/j.ins.2016.08.009 – volume: 450 start-page: 200 year: 2018 ident: 5777_CR23 publication-title: Inf Sci doi: 10.1016/j.ins.2018.03.031 – volume: 20 start-page: 21 issue: 1 year: 2011 ident: 5777_CR8 publication-title: Vldb J doi: 10.1007/s00778-010-0189-3 – volume: 24 start-page: 10 issue: 1 year: 2007 ident: 5777_CR17 publication-title: Appl Res Comput – ident: 5777_CR11 – ident: 5777_CR4 doi: 10.1145/342009.335388 – ident: 5777_CR39 doi: 10.1016/j.patrec.2016.05.007 – volume: 31 start-page: 651 issue: 8 year: 2010 ident: 5777_CR20 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2009.09.011 – volume: 24 start-page: 1273 issue: 9 year: 2002 ident: 5777_CR31 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2002.1033218 – volume: 6 start-page: 233 issue: 1 year: 1989 ident: 5777_CR3 publication-title: J Classif doi: 10.1007/BF01908601 |
| SSID | ssj0004685 |
| Score | 2.3517277 |
| Snippet | Clustering is an important part of data mining. The existing clustering algorithm failed in the data set with uneven density distribution. In this paper, we... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 10141 |
| SubjectTerms | Algorithms Artificial Intelligence Clustering Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data mining Data Mining and Knowledge Discovery Data points Datasets Density Density distribution Image Processing and Computer Vision Original Article Outliers (statistics) Probability and Statistics in Computer Science |
| SummonAdditionalLinks | – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8MwDI7Q4MCF8RSDgXLgBpGaNKHpESEmThMSD-1WksaFSWVDazdp_54kffAQIEGlnPJoayexnXy2EToBLY01CxhhAhRx0V-IpMIQLjMWCW5SDdInm4iGQzkaxTe1U1jRoN2bK0m_U7fObu4E05q-rogoiojdeFeFizbjbPTbhw_ekD4Rp7VbHKaHh7WrzPdjfBZH7zrml2tRL20G3f995ybaqLVLfFFNhy20ApNt1G0yN-B6Ie-gxwoDtwBsHIC9XBInzQxO87kLnGBfh1X-NJ2Ny-cXbNVaPPb-vN4nChsP5Wi7Np0KXGFDbG2-3EX3g6u7y2tS51ogqV2EJREQQyYVFwEDsALMKiY0UDxN7d8ApZkBxcPMhPYBqayWFlKmz8HSV7oElDTcQ53JdAL7CFumR4qKLNZacwhUnEmtTKg1s4Z4xkwP0YbkSVoHInf5MPKkDaHsSZgErjgSJqyHTts-r1UYjl9b9xtOJvWSLBJnCXPpznp66Kzh3Hv1z6Md_K35IVpnnvkOJNhHnXI2hyO0li7KcTE79lP1DZJR42M priority: 102 providerName: Springer Nature |
| Title | Relative density-based clustering algorithm for identifying diverse density clusters effectively |
| URI | https://link.springer.com/article/10.1007/s00521-021-05777-2 https://www.proquest.com/docview/2549482027 |
| Volume | 33 |
| WOSCitedRecordID | wos000628486900003&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: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1433-3058 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: P5Z dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1433-3058 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 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/eLvHCXMwpV1LT8MwDLZgcODCGzFeyoEbRLRpS7MTAgTiNE28hLiUpEkBaQzYOqT9e-w03QAJLlRKD02TRrFT24ntD2DXamnQLBBcJFZxyv7CZZgYHstCpElscm2lA5tI2215d9fq-A23gXerrP-J7kdtXnPaIz8gQyaWZKofvb1zQo2i01UPoTENM5SpDPl85uSs3bn8EhnpQDnRhiH_njjyYTMueI52RPEplSRNUy6-i6aJvvnjiNRJnvOF_455Eea9zsmOKyZZginbW4aFGs-B-eW9Ag-VZ9yHZYbc2ssRJxlnWN4dUjoFHBxT3Uf8QPn0wlDZZc8uytdFSjHjHDzGTetGA1Z5jGBtd7QKN-dn16cX3CMw8ByXZskT27KFVHESCGtRrKG6EgYqznOcOxuGhbEqjgoT4WWlQt0tCoU-tDjTkmApw2gNGr3Xnl0HhqyQqjApWlrr2AaqVUitTKS1QPO8EKYJYT35We7TkxNKRjcbJ1Z2BMsCKkSwTDRhb9zmrUrO8efbWzWVMr9QB9mERE3Yr-k8qf69t42_e9uEOeFYi1wFt6BR9od2G2bzj_J50N_xbLoD053kHu-XV7efy-vw4w |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB7RpVK5lL4QW6D1oT21VhPHIc6hqoCCQNAVqqjELdjxpEValscG0P6p_sbOOMluW6ncODSSL3FsyfaXGY89Mx_AG3TGk1mgpErRSs7-Ik2ceqlNpbJU-9KhCWQT2WBgjo_zwzn42cXCsFtlJxODoPbnJZ-Rf2BDRhs21T9dXEpmjeLb1Y5Co4HFPk5uyWQbf9z7TOv7Vqmd7aOtXdmyCsiS4FbLFHOsjNVppBBJVJMKjiOry5LwiHFcebQ6qXxCDxpL-5EkVm4dSXUbplqME-r3AczrRK-nPZjf3B4cfv0tEjOQgJLNxP5EOmnDdEKwHp_A0lsuaZZlUv2pCmf727-uZIOm21n83-boCTxu99Rio_kJnsIcjp7BYsdXIVrx9RxOGs-_GxSe3fbriWQd7kU5vOZ0ETQZwg6_04DqH2eCNvPiNEQxh0gw4YMDy7Rp12gsGo8Yqh1OXsC3exnnEvRG5yNcBkFQz2ycVrlzTmNk88o46xPnVB5llfJ9iLvFLso2_TqzgAyLaeLoAJAi4sIAKVQf3k3bXDTJR-78erVDRdEKonExg0Qf3ne4mlX_u7eXd_f2Gh7tHn05KA72BvsrsKACrNktchV69dU1rsHD8qY-HV-9an8RASf3jbhfgexJOw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwEA-iIr44P3E6NQ--adiapjZ9FHUoyhh-sbeaNKkO6ja2brD_3kv6sSkqiIU85aPp5dK7S353h9CxllyBWUAJ9bQgJvoL4Y6nCOMx9T2mIqm5TTbht1q80wnac178Fu1eXElmPg0mSlMvrQ9UXC8d38xpJpjBpni-7xP4CS8xsGQMqOv-4XnOM9Im5QQbxuB7mJu7zXw_xmfRNNM3v1yRWsnTrPx_zutoLdc68XnGJhtoQfc2UaXI6IDzDb6FXjJs3ERjZYDt6ZQYKadwlIxNQAV4NRbJa3_YTd_eMai7uGv9fK2vFFYW4lF2LTqNcIYZgdpkuo2emlePF9ckz8FAIticKfF0oGMumNegWoNgA4XFaQgWRfA12nFipQVzY-XCo7kA7c11qDzTQGtuElM67g5a7PV7ehdhYAZfOF4cSCmZbogg5lIoV0oKBnpMVRU5BfnDKA9QbvJkJGEZWtmSMGyYYkgY0io6KfsMsvAcv7auFasa5lt1FBoLmXFzBlRFp8Uqzqp_Hm3vb82P0Er7shne3bRu99EqtXxgcIQ1tJgOx_oALUeTtDsaHloO_gA4Nu8r |
| 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=Relative+density-based+clustering+algorithm+for+identifying+diverse+density+clusters+effectively&rft.jtitle=Neural+computing+%26+applications&rft.au=Wang%2C+Yuying&rft.au=Yang%2C+Youlong&rft.date=2021-08-01&rft.pub=Springer+Nature+B.V&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=33&rft.issue=16&rft.spage=10141&rft.epage=10157&rft_id=info:doi/10.1007%2Fs00521-021-05777-2 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon |