RETRACTED ARTICLE: Innovative study on clustering center and distance measurement of K-means algorithm: mapreduce efficient parallel algorithm based on user data of JD mall
The traditional K-means algorithm is very sensitive to the selection of clustering centers and the calculation of distances, so the algorithm easily converges to a locally optimal solution. In addition, the traditional algorithm has slow convergence speed and low clustering accuracy, as well as memo...
Uložené v:
| Vydané v: | Electronic commerce research Ročník 23; číslo 1; s. 43 - 73 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
New York
Springer US
01.03.2023
Springer Springer Nature B.V |
| Predmet: | |
| ISSN: | 1389-5753, 1572-9362 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The traditional K-means algorithm is very sensitive to the selection of clustering centers and the calculation of distances, so the algorithm easily converges to a locally optimal solution. In addition, the traditional algorithm has slow convergence speed and low clustering accuracy, as well as memory bottleneck problems when processing massive data. Therefore, an improved K-means algorithm is proposed in this paper. In this algorithm, the selection of the initial points in the traditional clustering algorithm is improved first, and then a new global measure, the effective distance measure, is proposed. Its main idea is to calculate the effective distance between two data samples by sparse reconstruction. Finally, on the basis of the MapReduce framework, the efficiency of the algorithm is further improved by adjusting the Hadoop cluster. Based on the real customer data from the JD Mall dataset, this paper introduces the DBI, Rand and other indicators to evaluate the clustering effects of various algorithms. The results show that the proposed algorithm not only has good convergence and accuracy but also achieves better performances than those of other compared algorithms. |
|---|---|
| AbstractList | The traditional K-means algorithm is very sensitive to the selection of clustering centers and the calculation of distances, so the algorithm easily converges to a locally optimal solution. In addition, the traditional algorithm has slow convergence speed and low clustering accuracy, as well as memory bottleneck problems when processing massive data. Therefore, an improved K-means algorithm is proposed in this paper. In this algorithm, the selection of the initial points in the traditional clustering algorithm is improved first, and then a new global measure, the effective distance measure, is proposed. Its main idea is to calculate the effective distance between two data samples by sparse reconstruction. Finally, on the basis of the MapReduce framework, the efficiency of the algorithm is further improved by adjusting the Hadoop cluster. Based on the real customer data from the JD Mall dataset, this paper introduces the DBI, Rand and other indicators to evaluate the clustering effects of various algorithms. The results show that the proposed algorithm not only has good convergence and accuracy but also achieves better performances than those of other compared algorithms. |
| Audience | Academic |
| Author | Du, Xinxin Liu, Yang Ma, Shuaifeng |
| Author_xml | – sequence: 1 givenname: Yang orcidid: 0000-0002-2568-6681 surname: Liu fullname: Liu, Yang email: statsyangliu@163.com organization: School of Statistics, Southwestern University of Finance and Economics – sequence: 2 givenname: Xinxin surname: Du fullname: Du, Xinxin organization: Big Data Operation Center, Jingdong Century Trading Co., Ltd – sequence: 3 givenname: Shuaifeng surname: Ma fullname: Ma, Shuaifeng organization: Big Data Operation Center, Jingdong Century Trading Co., Ltd |
| BookMark | eNp9kc9q3DAQh01JoUnaF-hJ0LPTkeS_uS2bbbvpQmHZnoUsjbYKtuRKdiD7TH3IauNCoIegg4bR75tBfFfZhfMOs-wjhRsKUH-OFKoKcmA0h7Yom_z0JrukZc3yllfsItW8afOyLvm77CrGBwAGNSsusz_7zWG_Wh82d2S1P2zXu80t2TrnH-VkH5HEadZPxDui-jlOGKw7EoUuVUQ6TbSNk3QKyYAyzgGH9ES8Id_z1HCRyP7og51-DbdkkGNAPacsGmOVPSdHGWTfY_-SI52MqM8L55h2aDnJ87z7u8T3_fvsrZF9xA__7uvs55fNYf0t3_34ul2vdrliwE85LZimlGEDXd1pbSppDDLsgHetNEhV05RcAZe6aotCdazpJNQd43XLDWuAX2eflrlj8L9njJN48HNwaaVgdQtV3RbQptTNkjrKHoV1xk9BqnQ0DlYlP8am_qouGmBlAWUC2AKo4GMMaMQY7CDDk6AgzhrFolEkjeJZozglqPkPUnZKcrxL22z_OsoXNI5ncRhevvEK9RcWkLZe |
| CitedBy_id | crossref_primary_10_3390_app15084470 crossref_primary_10_1016_j_jenvman_2023_118756 |
| Cites_doi | 10.1145/335191.335388 10.1126/science.1136800 10.1016/j.patcog.2008.05.018 10.1016/j.patrec.2009.09.011 10.1007/978-3-642-23713-3_9 10.1016/j.eswa.2012.07.021 10.1016/j.compbiomed.2010.06.007 10.1007/s11042-016-3322-5 10.1108/BFJ-08-2016-0354 10.1109/TCE.2011.5955230 10.1109/TKDE.2014.2316512 10.1016/j.camwa.2006.03.033 10.1016/S0165-0114(98)00403-5 10.1016/j.patrec.2017.09.025 10.1016/S0167-8655(97)00168-2 10.1126/science.1242072 10.1016/j.camwa.2007.08.031 10.1080/03081070600687668 10.1109/TNN.2005.845141 10.1109/TKDE.2017.2650229 10.1109/TCYB.2017.2702343 10.1016/j.neucom.2012.12.074 10.1016/j.procs.2015.06.090 10.1504/IJBIS.2018.093659 10.1109/IWBIS.2016.7872895 10.1145/3219819.3220039 10.1137/1.9781611975482.183 10.1109/WCCCT.2016.42 10.1109/WCSP.2017.8170955 10.1109/ICeND.2014.6991363 10.1137/1.9781611975994.138 10.1109/ICPADS.2011.83 10.1109/TPAMI.1979.4766909 10.1109/DCABES.2015.132 10.1007/11590316_124 10.1109/ICAICT.2017.8687081 10.1109/NAFIPS.2009.5156398 10.24963/ijcai.2017/210 10.1109/ICDM.2004.10100 10.1109/CCBD.2016.059 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. COPYRIGHT 2023 Springer |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: COPYRIGHT 2023 Springer |
| DBID | AAYXX CITATION 3V. 7SC 7TA 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ HCIFZ JG9 JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI Q9U |
| DOI | 10.1007/s10660-021-09458-z |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts Materials Business File ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection 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 Edition) ProQuest Central (Alumni) ProQuest Central UK/Ireland Health Research Premium Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology collection ProQuest One Community College ProQuest Central Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection Materials Research Database 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 Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic 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 Basic |
| DatabaseTitle | CrossRef Materials Research Database ABI/INFORM Global (Corporate) ProQuest Business Collection (Alumni Edition) ProQuest One Business Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Pharma Collection ABI/INFORM Complete Materials Business File ProQuest Central ABI/INFORM Professional Advanced ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection ProQuest Business Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Business (Alumni) ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) Business Premium Collection (Alumni) |
| DatabaseTitleList | Materials Research Database |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Business |
| EISSN | 1572-9362 |
| EndPage | 73 |
| ExternalDocumentID | A748025405 10_1007_s10660_021_09458_z |
| Genre | Correction/Retraction |
| GroupedDBID | -57 -5G -BR -EM -Y2 -~C .86 .VR 06D 0R~ 0VY 1N0 1SB 203 29G 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5VS 67Z 6NX 7WY 8AO 8FE 8FG 8FL 8FW 8TC 8UJ 8VB 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTD ABFTV ABHQN ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHQT ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHQJS AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKVCP ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AYQZM AZFZN AZQEC B-. BA0 BAPOH BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBO EBS EBU 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 GROUPED_ABI_INFORM_RESEARCH GXS H13 HCIFZ HF~ HG5 HG6 HLICF HMJXF HQYDN HRMNR HVGLF HZ~ I09 IAO IHE IJ- IKXTQ ITC ITG ITH ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K1G K60 K6V K6~ K7- KDC KOV LAK LLZTM M0C M0N M4Y MA- N2Q N9A NB0 NPVJJ NQJWS NU0 O9- O93 O9G O9J OAM OVD P2P P62 P9M PF0 PQBIZ PQBZA PQQKQ PROAC PT4 Q2X QOS QWB R89 R9I RNI ROL RPX RSV RZC RZD S16 S1Z S27 S3B SAP SBE SDH SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TEORI TH9 TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z81 Z83 ZL0 ZMTXR ~8M ~A9 AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION ICD PHGZM PHGZT PQGLB 7SC 7TA 7XB 8AL 8FD 8FK JG9 JQ2 L.- L7M L~C L~D PKEHL PQEST PQUKI Q9U |
| ID | FETCH-LOGICAL-c203z-142d112e80b7bddf6affe2eb03b9afe1c8853c03ad6944cb28ba07b23793f2803 |
| IEDL.DBID | 7WY |
| ISSN | 1389-5753 |
| IngestDate | Tue Dec 02 16:28:33 EST 2025 Sat Nov 29 10:28:13 EST 2025 Tue Nov 18 20:15:37 EST 2025 Sat Nov 29 01:37:47 EST 2025 Fri Feb 21 02:43:49 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | K-means Parallel computing Distance measurement Clustering center MapReduce |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c203z-142d112e80b7bddf6affe2eb03b9afe1c8853c03ad6944cb28ba07b23793f2803 |
| Notes | ObjectType-Correction/Retraction-1 SourceType-Scholarly Journals-1 content type line 14 |
| ORCID | 0000-0002-2568-6681 |
| PQID | 2790679409 |
| PQPubID | 25737 |
| PageCount | 31 |
| ParticipantIDs | proquest_journals_2790679409 gale_infotracacademiconefile_A748025405 crossref_primary_10_1007_s10660_021_09458_z crossref_citationtrail_10_1007_s10660_021_09458_z springer_journals_10_1007_s10660_021_09458_z |
| PublicationCentury | 2000 |
| PublicationDate | 20230300 2023-03-00 20230301 |
| PublicationDateYYYYMMDD | 2023-03-01 |
| PublicationDate_xml | – month: 3 year: 2023 text: 20230300 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | Electronic commerce research |
| PublicationTitleAbbrev | Electron Commer Res |
| PublicationYear | 2023 |
| Publisher | Springer US Springer Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer – name: Springer Nature B.V |
| References | Huang, Wang, Lai (CR15) 2018; 48 Partalidou, Koutsou (CR44) 2012; 7 Shi, Luo (CR40) 2010; 40 Sridharan, Sivakumar (CR43) 2018; 28 CR36 CR34 CR33 CR32 Guang, Liu, Zhang (CR12) 2017; 11 CR31 Xu, Ii (CR59) 2005; 16 Liang, Shi, Wierman (CR53) 2008; 35 Lei, Jiang, Wu, Du, Zhu, Wang (CR26) 2016; 75 CR2 CR4 Jianbin, Jianmei, Junjie, Heli (CR24) 2013; 43 Breunig, Kriegel, Ng, Sander (CR5) 2000; 29 CR9 CR49 CR47 Tleis, Callieris, Roma (CR48) 2017; 119 CR41 Dhanachandra, Manglem, Chanu (CR14) 2015; 54 Qian, Liang, Dang (CR35) 2008 Celebi, Kingravi, Vela (CR6) 2013; 40 Patrick, Krzysztof (CR30) 2001; 120 Siddiqui, Mat Isa (CR42) 2011; 57 Jain (CR23) 2010; 31 CR19 CR18 CR58 CR13 CR57 Rodriguez, Laio (CR39) 2014; 344 CR56 CR11 CR10 Frey, Dueck (CR17) 2007; 315 Rezaee, Lelieveldt, Reiber (CR37) 1998 CR51 Wu, Liu, Xiong, Cao, Chen (CR52) 2014; 27 CR50 Chen, Wang (CR8) 2006; 52 Liu, Wu, Liu, Tao, Fu (CR27) 2017; 29 Parmar, Wu, Blackhurst (CR29) 2007 Xiao-bin, Jiu-lun, Zhao (CR55) 2014; 134 Strehl, Ghosh (CR45) 2002; 3 Fan, Sun, Luo (CR16) 2017; 28 Xiang, Nie, Zhang (CR54) 2008; 41 Chakraborty, Das (CR7) 2017; 100 CR28 Albanese, Pal, Petrosino (CR3) 2011 CR25 CR22 CR21 Chakraborty, Das (CR1) 2017; 100 CR60 Tal (CR46) 2015; 22 Guo, Gao, Liu, Yin (CR20) 2017; 14 Rodriguez, Laio (CR38) 2014; 344 9458_CR21 AK Jain (9458_CR23) 2010; 31 S Xiang (9458_CR54) 2008; 41 MM Breunig (9458_CR5) 2000; 29 Y Qian (9458_CR35) 2008 9458_CR60 D Parmar (9458_CR29) 2007 N Dhanachandra (9458_CR14) 2015; 54 9458_CR28 S Chakraborty (9458_CR1) 2017; 100 9458_CR22 9458_CR25 J Shi (9458_CR40) 2010; 40 9458_CR32 9458_CR31 A Rodriguez (9458_CR39) 2014; 344 JG Patrick (9458_CR30) 2001; 120 CB Chen (9458_CR8) 2006; 52 ME Celebi (9458_CR6) 2013; 40 J Guang (9458_CR12) 2017; 11 FU Siddiqui (9458_CR42) 2011; 57 S Chakraborty (9458_CR7) 2017; 100 9458_CR34 9458_CR33 9458_CR36 J Liang (9458_CR53) 2008; 35 9458_CR41 Z Xiao-bin (9458_CR55) 2014; 134 MR Rezaee (9458_CR37) 1998 M Tleis (9458_CR48) 2017; 119 D Huang (9458_CR15) 2018; 48 J Wu (9458_CR52) 2014; 27 R Xu (9458_CR59) 2005; 16 A Rodriguez (9458_CR38) 2014; 344 G Tal (9458_CR46) 2015; 22 9458_CR49 H Jianbin (9458_CR24) 2013; 43 J Lei (9458_CR26) 2016; 75 9458_CR47 9458_CR51 9458_CR10 9458_CR50 H Liu (9458_CR27) 2017; 29 X Guo (9458_CR20) 2017; 14 9458_CR9 M Partalidou (9458_CR44) 2012; 7 A Strehl (9458_CR45) 2002; 3 BJ Frey (9458_CR17) 2007; 315 K Sridharan (9458_CR43) 2018; 28 9458_CR19 Z Fan (9458_CR16) 2017; 28 A Albanese (9458_CR3) 2011 9458_CR18 9458_CR2 9458_CR56 9458_CR4 9458_CR11 9458_CR58 9458_CR13 9458_CR57 |
| References_xml | – volume: 28 start-page: 195 issue: 6 year: 2017 end-page: 203 ident: CR16 article-title: Clustering of college students based on improved K-means algorithm publication-title: Journal of Computers (Taiwan) – year: 2007 ident: CR29 publication-title: MMR: An algorithm for clustering categorical data using rough set theory – ident: CR22 – ident: CR49 – volume: 11 start-page: 406 issue: 3 year: 2017 end-page: 413 ident: CR12 article-title: Application of effective distance in clustering algorithm publication-title: Computer Science and Exploration – ident: CR4 – volume: 29 start-page: 93 issue: 2 year: 2000 end-page: 104 ident: CR5 article-title: LOF: Identifying density-based local outliers publication-title: Acm Sigmod Record doi: 10.1145/335191.335388 – volume: 315 start-page: 972 issue: 5814 year: 2007 end-page: 976 ident: CR17 article-title: Clustering by passing messages between data points publication-title: Science doi: 10.1126/science.1136800 – ident: CR51 – volume: 41 start-page: 3600 issue: 12 year: 2008 end-page: 3612 ident: CR54 article-title: Learning a Mahalanobis distance measure for data clustering and classification publication-title: Pattern Recognition doi: 10.1016/j.patcog.2008.05.018 – volume: 31 start-page: 651 issue: 8 year: 2010 end-page: 666 ident: CR23 article-title: Data clustering: 50 years beyond K-means publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2009.09.011 – volume: 3 start-page: 583 issue: 3 year: 2002 end-page: 617 ident: CR45 article-title: Cluster ensembles: A knowledge reuse framework for combining multiple partitions publication-title: Journal of Machine Learning Research – ident: CR58 – volume: 7 start-page: 99 issue: 1 year: 2012 end-page: 116 ident: CR44 article-title: Locally and socially embedded tourism clusters in rural Greece publication-title: Tourismos: An International Multidisciplinary Journal of Tourism – ident: CR25 – year: 2011 ident: CR3 publication-title: A rough set approach to spatio-temporal outlier detection doi: 10.1007/978-3-642-23713-3_9 – volume: 40 start-page: 200 issue: 1 year: 2013 end-page: 210 ident: CR6 article-title: A comparative study of efficient initialization methods for the k-means clustering algorithm publication-title: Expert Systems with Applications: An International Journal doi: 10.1016/j.eswa.2012.07.021 – volume: 40 start-page: 723 issue: 8 year: 2010 end-page: 732 ident: CR40 article-title: Nonlinear dimensionality reduction of gene expression data for visualization and clustering analysis of cancer tissue samples publication-title: Computers in Biology & Medicine doi: 10.1016/j.compbiomed.2010.06.007 – ident: CR21 – ident: CR19 – volume: 75 start-page: 12043 issue: 19 year: 2016 end-page: 12059 ident: CR26 article-title: Robust K-means algorithm with automatically splitting and merging clusters and its applications for surveillance data publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-016-3322-5 – volume: 119 start-page: 1423 issue: 7 year: 2017 end-page: 1441 ident: CR48 article-title: Segmenting the organic food market in Lebanon: An application of K-means cluster analysis publication-title: British Food Journal doi: 10.1108/BFJ-08-2016-0354 – ident: CR50 – ident: CR11 – volume: 57 start-page: 833 issue: 2 year: 2011 end-page: 841 ident: CR42 article-title: Enhanced moving K-means (EMKM) algorithm for image segmentation publication-title: IEEE Transactions on Consumer Electronics doi: 10.1109/TCE.2011.5955230 – volume: 27 start-page: 155 issue: 1 year: 2014 end-page: 169 ident: CR52 article-title: K-means-based consensus clustering: A unified view publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2014.2316512 – volume: 52 start-page: 1563 issue: 10–11 year: 2006 end-page: 1576 ident: CR8 article-title: Rough set-based clustering with refinement using Shannon’s entropy theory publication-title: Computers and Mathematics with Applications doi: 10.1016/j.camwa.2006.03.033 – ident: CR9 – volume: 43 start-page: 599 issue: 5 year: 2013 end-page: 610 ident: CR24 article-title: A hierarchical clustering method based on a dynamic synchronization model publication-title: Chinese Science: Information Science – ident: CR57 – ident: CR32 – ident: CR60 – ident: CR36 – volume: 120 start-page: 227 issue: 2 year: 2001 end-page: 237 ident: CR30 article-title: Fuzzy clustering with squared Minkowski distances publication-title: Fuzzy Sets and Systems. doi: 10.1016/S0165-0114(98)00403-5 – volume: 100 start-page: 67 issue: 1 year: 2017 end-page: 73 ident: CR7 article-title: K means Clustering with a New divergence-based distance measure: convergence and performance analysis publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2017.09.025 – volume: 22 start-page: 3718 year: 2015 end-page: 3720 ident: CR46 article-title: dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering publication-title: Bioinformatics – ident: CR18 – ident: CR47 – volume: 100 start-page: 67 year: 2017 end-page: 73 ident: CR1 article-title: k Means clustering with a new divergence-based distance measure: Convergence and performance analysis publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2017.09.025 – ident: CR2 – year: 1998 ident: CR37 publication-title: A new cluster validity index for the fuzzy c-mean doi: 10.1016/S0167-8655(97)00168-2 – volume: 344 start-page: 1492 issue: 6191 year: 2014 end-page: 1496 ident: CR39 article-title: Clustering by fast search and find of density peaks publication-title: Science. doi: 10.1126/science.1242072 – ident: CR10 – ident: CR33 – year: 2008 ident: CR35 publication-title: Converse approximation and rule extraction from decision tables in rough set theory doi: 10.1016/j.camwa.2007.08.031 – ident: CR56 – volume: 35 start-page: 641 issue: 6 year: 2008 end-page: 654 ident: CR53 article-title: Information entropy, rough entropy and knowledge granulation in incomplete information systems publication-title: International Journal ofGeneral Systems doi: 10.1080/03081070600687668 – volume: 16 start-page: 645 issue: 3 year: 2005 end-page: 678 ident: CR59 article-title: Survey of clustering algorithms publication-title: IEEE Transactions on Neural Networks doi: 10.1109/TNN.2005.845141 – volume: 29 start-page: 1129 issue: 5 year: 2017 end-page: 1143 ident: CR27 article-title: Spectral ensemble clustering via weighted K-Means: theoretical and practical evidence publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2017.2650229 – volume: 48 start-page: 1460 issue: 5 year: 2018 end-page: 1473 ident: CR15 article-title: Locally Weighted Ensemble Clustering publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2017.2702343 – volume: 134 start-page: 20 year: 2014 end-page: 29 ident: CR55 article-title: Robust local feature weighting hard c-means clustering algorithm publication-title: Neurocomputing. doi: 10.1016/j.neucom.2012.12.074 – volume: 54 start-page: 764 year: 2015 end-page: 771 ident: CR14 article-title: Image segmentation using K-means clustering algorithm and subtractive clustering algorithm publication-title: Procedia Computer Science doi: 10.1016/j.procs.2015.06.090 – ident: CR31 – ident: CR13 – volume: 344 start-page: 1492 issue: 6191 year: 2014 end-page: 1496 ident: CR38 article-title: Machine learning. Clustering by fast search and find of density peaks publication-title: Science. doi: 10.1126/science.1242072 – ident: CR34 – volume: 14 start-page: 1753 issue: 1 year: 2017 end-page: 1759 ident: CR20 article-title: Improved Deep Embedded Clustering with Local Structure Preservation publication-title: Ijcai – volume: 28 start-page: 504 issue: 4 year: 2018 end-page: 518 ident: CR43 article-title: A systematic review on techniques of feature selection and classification for text mining publication-title: International Journal of Business Information Systems doi: 10.1504/IJBIS.2018.093659 – ident: CR28 – ident: CR41 – volume: 43 start-page: 599 issue: 5 year: 2013 ident: 9458_CR24 publication-title: Chinese Science: Information Science – ident: 9458_CR25 doi: 10.1109/IWBIS.2016.7872895 – volume: 3 start-page: 583 issue: 3 year: 2002 ident: 9458_CR45 publication-title: Journal of Machine Learning Research – volume: 11 start-page: 406 issue: 3 year: 2017 ident: 9458_CR12 publication-title: Computer Science and Exploration – volume: 119 start-page: 1423 issue: 7 year: 2017 ident: 9458_CR48 publication-title: British Food Journal doi: 10.1108/BFJ-08-2016-0354 – volume: 29 start-page: 93 issue: 2 year: 2000 ident: 9458_CR5 publication-title: Acm Sigmod Record doi: 10.1145/335191.335388 – ident: 9458_CR9 doi: 10.1145/3219819.3220039 – volume: 48 start-page: 1460 issue: 5 year: 2018 ident: 9458_CR15 publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2017.2702343 – ident: 9458_CR18 doi: 10.1137/1.9781611975482.183 – volume-title: MMR: An algorithm for clustering categorical data using rough set theory year: 2007 ident: 9458_CR29 – volume: 344 start-page: 1492 issue: 6191 year: 2014 ident: 9458_CR39 publication-title: Science. doi: 10.1126/science.1242072 – ident: 9458_CR34 doi: 10.1109/WCCCT.2016.42 – ident: 9458_CR56 – volume: 315 start-page: 972 issue: 5814 year: 2007 ident: 9458_CR17 publication-title: Science doi: 10.1126/science.1136800 – ident: 9458_CR41 doi: 10.1109/WCSP.2017.8170955 – ident: 9458_CR33 – ident: 9458_CR36 doi: 10.1109/ICeND.2014.6991363 – volume-title: A new cluster validity index for the fuzzy c-mean year: 1998 ident: 9458_CR37 doi: 10.1016/S0167-8655(97)00168-2 – ident: 9458_CR4 – volume: 52 start-page: 1563 issue: 10–11 year: 2006 ident: 9458_CR8 publication-title: Computers and Mathematics with Applications doi: 10.1016/j.camwa.2006.03.033 – ident: 9458_CR11 doi: 10.1137/1.9781611975994.138 – volume: 31 start-page: 651 issue: 8 year: 2010 ident: 9458_CR23 publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2009.09.011 – ident: 9458_CR60 – volume: 29 start-page: 1129 issue: 5 year: 2017 ident: 9458_CR27 publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2017.2650229 – ident: 9458_CR22 doi: 10.1109/ICPADS.2011.83 – ident: 9458_CR13 doi: 10.1109/TPAMI.1979.4766909 – volume: 57 start-page: 833 issue: 2 year: 2011 ident: 9458_CR42 publication-title: IEEE Transactions on Consumer Electronics doi: 10.1109/TCE.2011.5955230 – ident: 9458_CR51 doi: 10.1109/DCABES.2015.132 – volume: 344 start-page: 1492 issue: 6191 year: 2014 ident: 9458_CR38 publication-title: Science. doi: 10.1126/science.1242072 – ident: 9458_CR57 – ident: 9458_CR28 doi: 10.1007/11590316_124 – volume: 134 start-page: 20 year: 2014 ident: 9458_CR55 publication-title: Neurocomputing. doi: 10.1016/j.neucom.2012.12.074 – volume: 28 start-page: 195 issue: 6 year: 2017 ident: 9458_CR16 publication-title: Journal of Computers (Taiwan) – volume-title: A rough set approach to spatio-temporal outlier detection year: 2011 ident: 9458_CR3 doi: 10.1007/978-3-642-23713-3_9 – volume: 120 start-page: 227 issue: 2 year: 2001 ident: 9458_CR30 publication-title: Fuzzy Sets and Systems. doi: 10.1016/S0165-0114(98)00403-5 – volume: 54 start-page: 764 year: 2015 ident: 9458_CR14 publication-title: Procedia Computer Science doi: 10.1016/j.procs.2015.06.090 – ident: 9458_CR32 – ident: 9458_CR47 doi: 10.1109/ICAICT.2017.8687081 – ident: 9458_CR19 – volume: 28 start-page: 504 issue: 4 year: 2018 ident: 9458_CR43 publication-title: International Journal of Business Information Systems doi: 10.1504/IJBIS.2018.093659 – ident: 9458_CR50 doi: 10.1109/NAFIPS.2009.5156398 – volume: 100 start-page: 67 issue: 1 year: 2017 ident: 9458_CR7 publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2017.09.025 – ident: 9458_CR10 doi: 10.24963/ijcai.2017/210 – ident: 9458_CR21 – volume: 40 start-page: 200 issue: 1 year: 2013 ident: 9458_CR6 publication-title: Expert Systems with Applications: An International Journal doi: 10.1016/j.eswa.2012.07.021 – ident: 9458_CR31 – volume: 75 start-page: 12043 issue: 19 year: 2016 ident: 9458_CR26 publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-016-3322-5 – volume: 100 start-page: 67 year: 2017 ident: 9458_CR1 publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2017.09.025 – volume: 27 start-page: 155 issue: 1 year: 2014 ident: 9458_CR52 publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2014.2316512 – volume: 14 start-page: 1753 issue: 1 year: 2017 ident: 9458_CR20 publication-title: Ijcai – volume: 22 start-page: 3718 year: 2015 ident: 9458_CR46 publication-title: Bioinformatics – ident: 9458_CR49 doi: 10.1109/ICDM.2004.10100 – volume: 35 start-page: 641 issue: 6 year: 2008 ident: 9458_CR53 publication-title: International Journal ofGeneral Systems doi: 10.1080/03081070600687668 – volume: 16 start-page: 645 issue: 3 year: 2005 ident: 9458_CR59 publication-title: IEEE Transactions on Neural Networks doi: 10.1109/TNN.2005.845141 – volume: 7 start-page: 99 issue: 1 year: 2012 ident: 9458_CR44 publication-title: Tourismos: An International Multidisciplinary Journal of Tourism – ident: 9458_CR2 – volume: 40 start-page: 723 issue: 8 year: 2010 ident: 9458_CR40 publication-title: Computers in Biology & Medicine doi: 10.1016/j.compbiomed.2010.06.007 – volume-title: Converse approximation and rule extraction from decision tables in rough set theory year: 2008 ident: 9458_CR35 doi: 10.1016/j.camwa.2007.08.031 – volume: 41 start-page: 3600 issue: 12 year: 2008 ident: 9458_CR54 publication-title: Pattern Recognition doi: 10.1016/j.patcog.2008.05.018 – ident: 9458_CR58 doi: 10.1109/CCBD.2016.059 |
| SSID | ssj0020724 |
| Score | 2.2766743 |
| Snippet | The traditional K-means algorithm is very sensitive to the selection of clustering centers and the calculation of distances, so the algorithm easily converges... |
| SourceID | proquest gale crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 43 |
| SubjectTerms | Algorithms Business and Management Computer Communication Networks Data Structures and Information Theory e-Commerce/e-business IT in Business Measurement Operations Research/Decision Theory Shopping malls |
| SummonAdditionalLinks | – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT9tAEF5RWlW99F01hVZzqMShXWmztuM1tygEFYoQAoq4rfZliuQ4yAkc8pv6IzvjrAl9Su3Vnn1IMzuP3ZlvGHtfWkk9kBLujLQ8lUZyYwKGKmgb-yaYwrTtfM4O8sNDdX5eHMWisFmX7d49Sbaa-k6x22AgOKUUYEiSKb64x-5nhDZDMfrJ2W2YJfLYylYVHJ2RJJbK_H6OH8zRz0r5l9fR1ujsPvm_7T5lj6OTCcOlVDxja6F-zh52Oe4v2Lfj8enxcIRKC9Cf3RsdjLdhL7ZHvQnQQs7CtAZXXROOAi4LlMQZGjC1B08uJ8oKTFb3izAt4TPHD_UMTHUxbS7nXyfbMDFXDWHDBggtVAVREth4VYVqRQdkSj0tSFcmQEmrNN_-Do6vqpfsy-74dPSJx7YN3CHTF7yfSo9eXFDC5tb7cmDKMshgRWILU4a-U-giOJEYPyjS1FmprBG5lQmqipKaZb1i6_W0Dq8Z2KRQLvfeZdKkwkuVeWGURT9N5jZJQo_1O-5pFzHNqbVGpVdozMQGjWzQLRv0osc-3I65WiJ6_JV6i4RC03HHmZ2JVQu4PwLO0sM8VQQoILIe2-zkRkc9MNMyL-imDoPoHvvYycnq95_XffNv5BvskUTva5kct8nW5811eMseuJv55ax5156P7896C6k priority: 102 providerName: Springer Nature |
| Title | RETRACTED ARTICLE: Innovative study on clustering center and distance measurement of K-means algorithm: mapreduce efficient parallel algorithm based on user data of JD mall |
| URI | https://link.springer.com/article/10.1007/s10660-021-09458-z https://www.proquest.com/docview/2790679409 |
| Volume | 23 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1572-9362 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020724 issn: 1389-5753 databaseCode: RSV dateStart: 20010201 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/eLvHCXMwpV1LbxMxELagRYgLb0SgVD4gcQALx7sbe3tBIaSitERRKKVwsfxaQNpsQpL2kN_Ej2Rm4-0KEL1wsbS7fkmenRmPx99HyNPCCuRASpgzwrJUGMGMCbBVAdvYNcHkpqbzOTmSo5E6Pc3HMeC2jGmVjU6sFbWfOYyRvxQyx5gHbEdezX8wZI3C09VIoXGVbIOhzpDBQH76fLHh4jKS2qqcgVuSxEsz8epcr8cZJijABidTbP2bYfpTPf91Tlqbn_1b_zvx2-RmdDxpfyMpd8iVUN0l15u893vk52R4POkPQJFR8HEPBkfDPXoQKVPPA61haOmsoq48Q2wFmCTFxM6woKby1KMbCvJDp23Mkc4KesjgRbWkpvwKc1p9m-7RqZkvEC820FDDV2BNBCAvy1C29SiaV48DYhiFYiIr9vfuDbQvy_vk4_7wePCWRSoH5kAQ1qybCg-eXVDcSut90TNFEUSwPLG5KULXKXAbHE-M7-Vp6qxQ1nBpRQLqo0ACrQdkq5pV4SGhNsmVk967TJiUe6Eyz42y4LsJaZMkdEi3WUftIs450m2UukVoxrXXsPa6Xnu97pDnF23mG5SPS2s_Q_HQqAKgZ2fiTQaYH4Jp6b5MFYIM8KxDdhqZ0FE3LHUrEB3yopGq9vO_x310eW-PyQ0BHtgmQW6HbK0WZ-EJuebOV9-Xi936z9gl26-Ho_EEng4lg_I9H0A5zr5AOflw8gugJxqG |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFD4aAzFeuE8UBvgBxANYuE7SJJMQqrpOKy3VNBU08WJ8CyClaWm7IfqbEL-Rc9JkFSD2tgdeE8eJnM_nYh9_H8CTzEjSQAq41dLwUGrJtfaYqqBvbGqvU13K-bwfxMNhcnycHm7Az_osDJVV1jaxNNRuYmmN_KWMU1rzwHTk9fQrJ9Uo2l2tJTRWsOj7798wZZu_6u3h_30q5X531DnglaoAt_hNS94MpcMgwyfCxMa5rKWzzEtvRGBSnfmmTdCDWRFo10rD0BqZGC1iIwNEckZaTtjvJbgchlKQYsJh9OEswRNxJaKbpBzDoKA6pFMd1Wu1BKeCCEyoooQvf3OEf7qDv_ZlS3e3f-N_G6ibcL0KrFl7NRNuwYYvbsPVuq7_Dvw46o6O2h001Axj-F5n0N1lvUoS9tSzkmaXTQpm8xPijsBBYVS46mdMF445CrNxfrDxek2VTTLW53ihmDOdf8IxWHwe77Kxns6ID9czX9JzUEsiWM9zn6_bMQofHL2QlokYFepSf2_28Pk8vwvvLmSstmGzmBT-HjATpImNnbOR1KFwMomc0InB2FTGJgh8A5o1bpSteNxJTiRXawZqwppCrKkSa2rZgOdnz0xXLCbntn5GcFRk4rBnq6uTGvh9RBam2nGYEImCiBqwU2NQVbZvrtYAbMCLGsXr2_9-7_3ze3sMWwejtwM16A37D-AaTr9gVQy4A5uL2Yl_CFfs6eLLfPaonJUMPl40un8BnihzFg |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFLbGQNNeuCMKA_wA4gGsuU7aOJMQqnoRpVU1TQPtzfgWhpSmpe2G6G_iF_DrOCd1FgFib3vgNXGcyPl8znfs4-8Q8jwzAmsgRcxqYVgstGBaewhVwDc2tdepLsv5fBwnk4k8OUkPt8jP6iwMplVWNrE01G5mcY18XyQprnlAOLKfhbSIw97g7fwrwwpSuNNaldPYQGTkv3-D8G35ZtiDf_1CiEH_uPuOhQoDzML3rVkzFg4Ih5fcJMa5rK2zzAtveGRSnfmmleDNLI-0a6dxbI2QRvPEiAhQnWFdJ-j3GrmexMAjMG2Qdy-CPZ6EgroyZUCJonBgJxzba7c5w-QICK5akq1_c4p_uoa_9mhL1ze49T8P2m1yMxBu2tnMkDtkyxd3yU6V73-P_DjqHx91umDAKXD7YXfcP6DDUCr23NNSfpfOCmrzM9SUgAGimNDqF1QXjjqk3zBv6LRea6WzjI4YXCiWVOefYQxWp9MDOtXzBerkeupL2Q5sicLree7zuh1FWuHwhbh8RDGBF_t734Pn8_w--XAlY_WAbBezwj8k1ESptIlztiV0zJ2QLce1NMBZRWKiyDdIs8KQskHfHcuM5KpWpkbcKcCdKnGn1g3y6uKZ-Ubd5NLWLxGaCk0f9Gx1OMEB34ciYqqTxBLFFXirQfYqPKpgE5eqBmODvK4QXd_-93sfXd7bM7IDoFbj4WT0mOwKIKGbHME9sr1anPkn5IY9X31ZLp6WE5SST1cN7l-UbXuf |
| 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=RETRACTED+ARTICLE%3A+Innovative+study+on+clustering+center+and+distance+measurement+of+K-means+algorithm%3A+mapreduce+efficient+parallel+algorithm+based+on+user+data+of+JD+mall&rft.jtitle=Electronic+commerce+research&rft.au=Liu%2C+Yang&rft.au=Du%2C+Xinxin&rft.au=Ma%2C+Shuaifeng&rft.date=2023-03-01&rft.pub=Springer+Nature+B.V&rft.issn=1389-5753&rft.eissn=1572-9362&rft.volume=23&rft.issue=1&rft.spage=43&rft.epage=73&rft_id=info:doi/10.1007%2Fs10660-021-09458-z&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1389-5753&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1389-5753&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1389-5753&client=summon |