Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm
The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO al...
Uložené v:
| Vydané v: | Applied soft computing Ročník 113; s. 107924 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.12.2021
|
| Predmet: | |
| ISSN: | 1568-4946, 1872-9681 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum; thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the K-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the K-means cluster centres (KM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare KM-ALPSO on five UCI datasets. Finally, the improved KM-ALPSO (IKM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IKM-ALPSO for customer segmentation.
•A new ALPSO algorithm is proposed to improve the optimization accuracy of PSO.•A new KM-ALPSO is proposed to avert the dependence of K-means on the initial centres.•A new IKM-ALPSO algorithm is proposed to address the issue of clustering mixed data.•All results show that the three proposed methods outperform existing models.•The results demonstrate the practicability of IKM-ALPSO for customer segmentation. |
|---|---|
| AbstractList | The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum; thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the K-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the K-means cluster centres (KM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare KM-ALPSO on five UCI datasets. Finally, the improved KM-ALPSO (IKM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IKM-ALPSO for customer segmentation.
•A new ALPSO algorithm is proposed to improve the optimization accuracy of PSO.•A new KM-ALPSO is proposed to avert the dependence of K-means on the initial centres.•A new IKM-ALPSO algorithm is proposed to address the issue of clustering mixed data.•All results show that the three proposed methods outperform existing models.•The results demonstrate the practicability of IKM-ALPSO for customer segmentation. |
| ArticleNumber | 107924 |
| Author | Tian, Dong Chu, Xiaoquan Li, Yue Mu, Weisong Feng, Jianying |
| Author_xml | – sequence: 1 givenname: Yue orcidid: 0000-0003-1392-2074 surname: Li fullname: Li, Yue email: 18800146617@163.com organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China – sequence: 2 givenname: Xiaoquan surname: Chu fullname: Chu, Xiaoquan email: chuxiaoquan1994@163.com organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China – sequence: 3 givenname: Dong orcidid: 0000-0002-4759-5334 surname: Tian fullname: Tian, Dong email: td_tiandong@cau.edu.cn organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China – sequence: 4 givenname: Jianying orcidid: 0000-0002-1048-7524 surname: Feng fullname: Feng, Jianying email: fjying@cau.edu.cn organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China – sequence: 5 givenname: Weisong surname: Mu fullname: Mu, Weisong email: wsmu@cau.edu.cn organization: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, PR China |
| BookMark | eNp9kM1KAzEUhYNUsK2-gKu8wNT8zHQm4EaKf1hwo-uQJjdtykymJGlFn94M48pFV_dyON-Bc2Zo4nsPCN1SsqCELu_2CxV7vWCE0SzUgpUXaEqbmhVi2dBJ_qtlU5SiXF6hWYx7kiHBmimC1TGmvoOAI2w78Ekl13t8jM5v8VvRgfIR6zabIAyS8ganHWBl1CG5E-CDCsnpFnD8UqHDfVY79zOmqHbbB5d23TW6tKqNcPN35-jz6fFj9VKs359fVw_rQnNCUrEphea23lDKOBEKVGOY5XWplICS2sbW3BJbUs6rihuia1ZzI0zFgQm9ydY5asZcHfoYA1ip3dgoBeVaSYkc5pJ7Ocwlh7nkOFdG2T_0EFynwvd56H6EIJc6OQgyagdeg3EBdJKmd-fwXzHmiKM |
| CitedBy_id | crossref_primary_10_1007_s10586_024_04456_w crossref_primary_10_32604_cmes_2023_026113 crossref_primary_10_7454_jessd_v7i2_1258 crossref_primary_10_1093_comjnl_bxab206 crossref_primary_10_1016_j_cie_2023_109524 crossref_primary_10_1016_j_jclepro_2023_137072 crossref_primary_10_1007_s11135_025_02259_8 crossref_primary_10_1016_j_engappai_2023_107378 crossref_primary_10_3389_fams_2025_1598165 crossref_primary_10_1007_s12065_022_00747_6 crossref_primary_10_4018_JOEUC_346230 crossref_primary_10_1016_j_cie_2022_108717 crossref_primary_10_2478_amns_2023_2_01368 crossref_primary_10_2298_CSIS240930019Z crossref_primary_10_1016_j_apenergy_2025_125339 crossref_primary_10_1109_ACCESS_2025_3529515 crossref_primary_10_3389_fphy_2025_1623161 crossref_primary_10_1016_j_compag_2023_108320 crossref_primary_10_3390_app122413019 crossref_primary_10_1007_s40745_024_00525_4 crossref_primary_10_32604_cmes_2023_029258 crossref_primary_10_1016_j_asoc_2022_109838 crossref_primary_10_3389_fbioe_2022_908356 crossref_primary_10_1016_j_dajour_2025_100630 crossref_primary_10_1080_17445302_2022_2111924 crossref_primary_10_1007_s00521_024_10621_4 crossref_primary_10_1007_s13246_024_01475_0 crossref_primary_10_1016_j_cemconres_2022_107003 crossref_primary_10_1016_j_asoc_2023_110201 crossref_primary_10_1016_j_oceaneng_2025_121546 crossref_primary_10_3390_app15020718 crossref_primary_10_1016_j_eswa_2023_120278 crossref_primary_10_1016_j_ins_2025_122414 crossref_primary_10_32604_cmc_2025_060765 crossref_primary_10_1007_s13042_023_01893_8 crossref_primary_10_1016_j_procs_2024_06_430 crossref_primary_10_3390_app12168310 crossref_primary_10_1057_s41270_022_00158_7 crossref_primary_10_1109_OJCS_2025_3601668 crossref_primary_10_3233_JIFS_231922 crossref_primary_10_33889_IJMEMS_2024_10_3_037 crossref_primary_10_3389_fdata_2024_1184444 crossref_primary_10_1016_j_eswa_2022_116982 crossref_primary_10_3390_smartcities6050107 crossref_primary_10_1016_j_engappai_2023_106351 crossref_primary_10_1051_e3sconf_202346502005 crossref_primary_10_1016_j_jretconser_2024_104121 crossref_primary_10_1016_j_asoc_2022_109619 crossref_primary_10_1108_K_01_2022_0049 crossref_primary_10_1007_s40747_024_01420_4 crossref_primary_10_3390_math11244985 crossref_primary_10_3390_en16062651 crossref_primary_10_1007_s41870_024_02279_x crossref_primary_10_3390_computers14030093 crossref_primary_10_1016_j_asoc_2024_111264 crossref_primary_10_1109_TPDS_2023_3243634 crossref_primary_10_1111_exsy_13486 crossref_primary_10_1111_jfpp_16343 crossref_primary_10_1016_j_asoc_2022_109780 crossref_primary_10_1016_j_asoc_2025_113699 crossref_primary_10_1007_s10489_024_05267_7 crossref_primary_10_1080_17457300_2023_2272242 crossref_primary_10_1108_DTA_08_2021_0222 crossref_primary_10_1007_s12008_024_01952_6 crossref_primary_10_1016_j_asoc_2024_111419 crossref_primary_10_3390_jtaer17020024 |
| Cites_doi | 10.1016/j.asoc.2019.105763 10.1016/j.patrec.2011.02.017 10.1016/j.neucom.2015.01.062 10.1016/j.jretconser.2018.04.010 10.1108/BFJ-06-2019-0465 10.1007/s10115-018-1162-5 10.1007/s10489-017-0984-2 10.1016/j.asoc.2014.06.034 10.1016/j.eswa.2008.01.039 10.1016/j.patrec.2009.09.011 10.1007/s12046-018-0823-0 10.1016/j.asoc.2017.02.004 10.1007/s00500-016-2102-5 10.1016/j.asoc.2015.07.005 10.1007/s00500-019-04507-0 10.1016/j.knosys.2018.08.037 10.1016/j.asoc.2016.07.041 10.1109/ACCESS.2019.2903568 10.2307/2528823 10.1016/j.asoc.2018.05.045 10.1016/j.asoc.2019.105653 10.1016/j.patrec.2008.01.021 10.1016/j.patcog.2007.09.009 10.1016/j.ins.2014.09.030 10.1016/j.chaos.2020.110170 10.1007/s10257-018-0381-3 10.1016/j.compeleceng.2015.05.019 10.1016/j.dss.2009.05.016 10.1016/j.asoc.2018.03.011 10.1016/j.neucom.2018.12.093 10.1016/j.ins.2018.04.061 10.1007/s10994-015-5522-z 10.1016/j.neucom.2011.12.009 10.1007/s10044-018-00772-1 10.1007/s00500-009-0539-5 10.1016/j.eswa.2009.02.003 10.1016/j.eswa.2010.06.038 10.1504/IJBIR.2014.059532 10.1016/j.patcog.2017.06.023 10.1016/j.asoc.2009.07.001 10.1016/j.asoc.2014.11.018 10.1016/j.ins.2020.08.121 10.1007/s00521-019-04417-0 10.1016/j.patcog.2012.07.021 10.1007/s10044-005-0015-5 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier B.V. |
| Copyright_xml | – notice: 2021 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.asoc.2021.107924 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-9681 |
| ExternalDocumentID | 10_1016_j_asoc_2021_107924 S1568494621008462 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 53G 5GY 5VS 6J9 7-5 71M 8P~ AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABFRF ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HVGLF HZ~ IHE J1W JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SST SSV SSZ T5K UHS UNMZH ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c300t-b49c3f7b112309aea8d2f374aa9e41f8f73f0f4133553d0c7273d9d53e29cb2f3 |
| ISICitedReferencesCount | 74 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000724862700008&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1568-4946 |
| IngestDate | Sat Nov 29 07:03:32 EST 2025 Tue Nov 18 21:45:30 EST 2025 Fri Feb 23 02:41:53 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Customer segmentation K-means clustering algorithm Mixed data Particle swarm optimization algorithm Adaptive parameter learning |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-b49c3f7b112309aea8d2f374aa9e41f8f73f0f4133553d0c7273d9d53e29cb2f3 |
| ORCID | 0000-0002-4759-5334 0000-0003-1392-2074 0000-0002-1048-7524 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_asoc_2021_107924 crossref_primary_10_1016_j_asoc_2021_107924 elsevier_sciencedirect_doi_10_1016_j_asoc_2021_107924 |
| PublicationCentury | 2000 |
| PublicationDate | December 2021 2021-12-00 |
| PublicationDateYYYYMMDD | 2021-12-01 |
| PublicationDate_xml | – month: 12 year: 2021 text: December 2021 |
| PublicationDecade | 2020 |
| PublicationTitle | Applied soft computing |
| PublicationYear | 2021 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Lin, Li, Kuo, Wang (b35) 2016; 56 Zhang, Zhang, Wang (b46) 2018; 454-455 Dorosti, Ghoushchi, Sobhrakhshankhah, Ahmadi, Sharifi (b6) 2020; 24 Mccane, Albert (b49) 2008; 29 Dong, Kang, Zhang (b32) 2017; 21 Park, Jun (b22) 2009; 36 Wang, Yang, Orchard (b27) 2016; 48 Ren, Liu, Wang, Pan (b17) 2016 Cao, Lu, Ye, Liang (b26) 2015; 35 Holý, Sokol, Černý (b44) 2017; 60 Kennedy (b24) 1995 Li, Zhou, Kou, Xiao (b30) 2012; 83 Aghdaie, Tafreshi, Behzadian (b2) 2014; 8 Jiang, Shi, Zhang, Han (b37) 2015; 158 Wang, Zhou, Xu (b41) 2018; 48 Zhou, Liu, Yang, Chen, Wu (b11) 2019; 163 Sun, Wu, Palade, Fang, Shi (b38) 2015; 101 Das, Das, Dey (b8) 2018; 70 Deng, Gao (b12) 2020; 18 Cortez, Cerdeira, Almeida, Matos, Reis (b42) 2009; 47 Yang, Sun, Zhang (b48) 2009; 36 Li, Chu, Mou, Tian, Feng, Mu (b51) 2020; vol. 1274 Xie, Zhang, Lim, Yu, Liu, Liu, Walters (b9) 2019; 84 Ai, Dong (b33) 2016; 36 Bai, Cheng, Liang, Shen, Guo (b20) 2017; 71 Jain (b14) 2010; 31 Omran, Salman, Engelbrecht (b29) 2006; 8 Chu, Li, Xie, Tian (b45) 2019; 122 Ahmad, Khan (b16) 2019; 7 Hakli, Uğuz (b40) 2014; 23 Calvo-Porral, Levy-Mangin (b3) 2018; 43 Zhang, Tang, Hua, Guan (b34) 2015; 28 Hassantabar, Stefano, Ghanakota, Ferrari, Nicola, Bruno, Marino, Jha (b7) 2020 Ahmad, Dey (b18) 2011; 32 Ahmadi, Jafarzadeh-Ghoushchi, Taghizadeh, Sharifi (b47) 2019; 31 Kuo, Wang, Huang (b39) 2011; 15 Kuo, Zulvia (b10) 2018; 57 Bouyer, Hatamlou (b28) 2018; 67 Arbelaitz, Gurrutxaga, Muguerza, Pérez, Perona (b43) 2013; 46 Tao, Gu, Wang, Jiang (b23) 2020; 393 Sangam, Om (b19) 2018; 43 Li, Zhan, Lin, Zhang, Luo (b36) 2015; 293 Isiet, Gadala (b25) 2019; 83 Ozertem, Erdogmus, Jenssen (b15) 2008; 41 Hassantabar, Ahmadi, Sharifi (b5) 2020; 140 Yoseph, Malim, Heikkilä, Brezulianu, Geman, Rostam (b4) 2020; 38 Niknam, Amiri (b13) 2010; 10 Gower (b50) 1971; 27 Wu (b31) 2011; 38 Ushakov, Vasilyev (b21) 2021; 545 Lee, Song, Cho, Choi (b1) 2019; 22 Lin (10.1016/j.asoc.2021.107924_b35) 2016; 56 Kennedy (10.1016/j.asoc.2021.107924_b24) 1995 Wang (10.1016/j.asoc.2021.107924_b27) 2016; 48 Hakli (10.1016/j.asoc.2021.107924_b40) 2014; 23 Omran (10.1016/j.asoc.2021.107924_b29) 2006; 8 Cao (10.1016/j.asoc.2021.107924_b26) 2015; 35 Hassantabar (10.1016/j.asoc.2021.107924_b7) 2020 Bouyer (10.1016/j.asoc.2021.107924_b28) 2018; 67 Ren (10.1016/j.asoc.2021.107924_b17) 2016 Das (10.1016/j.asoc.2021.107924_b8) 2018; 70 Zhang (10.1016/j.asoc.2021.107924_b34) 2015; 28 Wang (10.1016/j.asoc.2021.107924_b41) 2018; 48 Jiang (10.1016/j.asoc.2021.107924_b37) 2015; 158 Hassantabar (10.1016/j.asoc.2021.107924_b5) 2020; 140 Jain (10.1016/j.asoc.2021.107924_b14) 2010; 31 Li (10.1016/j.asoc.2021.107924_b36) 2015; 293 Ahmad (10.1016/j.asoc.2021.107924_b18) 2011; 32 Xie (10.1016/j.asoc.2021.107924_b9) 2019; 84 Dorosti (10.1016/j.asoc.2021.107924_b6) 2020; 24 Lee (10.1016/j.asoc.2021.107924_b1) 2019; 22 Mccane (10.1016/j.asoc.2021.107924_b49) 2008; 29 Tao (10.1016/j.asoc.2021.107924_b23) 2020; 393 Zhou (10.1016/j.asoc.2021.107924_b11) 2019; 163 Sun (10.1016/j.asoc.2021.107924_b38) 2015; 101 Cortez (10.1016/j.asoc.2021.107924_b42) 2009; 47 Chu (10.1016/j.asoc.2021.107924_b45) 2019; 122 Kuo (10.1016/j.asoc.2021.107924_b39) 2011; 15 Aghdaie (10.1016/j.asoc.2021.107924_b2) 2014; 8 Park (10.1016/j.asoc.2021.107924_b22) 2009; 36 Arbelaitz (10.1016/j.asoc.2021.107924_b43) 2013; 46 Ozertem (10.1016/j.asoc.2021.107924_b15) 2008; 41 Zhang (10.1016/j.asoc.2021.107924_b46) 2018; 454-455 Li (10.1016/j.asoc.2021.107924_b30) 2012; 83 Holý (10.1016/j.asoc.2021.107924_b44) 2017; 60 Gower (10.1016/j.asoc.2021.107924_b50) 1971; 27 Dong (10.1016/j.asoc.2021.107924_b32) 2017; 21 Bai (10.1016/j.asoc.2021.107924_b20) 2017; 71 Yoseph (10.1016/j.asoc.2021.107924_b4) 2020; 38 Calvo-Porral (10.1016/j.asoc.2021.107924_b3) 2018; 43 Sangam (10.1016/j.asoc.2021.107924_b19) 2018; 43 Ai (10.1016/j.asoc.2021.107924_b33) 2016; 36 Ahmadi (10.1016/j.asoc.2021.107924_b47) 2019; 31 Li (10.1016/j.asoc.2021.107924_b51) 2020; vol. 1274 Wu (10.1016/j.asoc.2021.107924_b31) 2011; 38 Ushakov (10.1016/j.asoc.2021.107924_b21) 2021; 545 Deng (10.1016/j.asoc.2021.107924_b12) 2020; 18 Niknam (10.1016/j.asoc.2021.107924_b13) 2010; 10 Yang (10.1016/j.asoc.2021.107924_b48) 2009; 36 Isiet (10.1016/j.asoc.2021.107924_b25) 2019; 83 Ahmad (10.1016/j.asoc.2021.107924_b16) 2019; 7 Kuo (10.1016/j.asoc.2021.107924_b10) 2018; 57 |
| References_xml | – volume: 67 start-page: 172 year: 2018 end-page: 182 ident: b28 article-title: An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms publication-title: Appl. Soft. Comput. – volume: 36 start-page: 687 year: 2016 end-page: 691 ident: b33 article-title: Improved particle swarm optimization algorithm based on Gaussian disturbance and natural selection publication-title: J. Comput. Appl. – volume: 29 start-page: 986 year: 2008 end-page: 993 ident: b49 article-title: Distance functions for categorical and mixed variables publication-title: Pattern Recognit. Lett. – volume: 8 start-page: 168 year: 2014 end-page: 189 ident: b2 article-title: Customer-oriented benefit segmentation: an integrated approach publication-title: Int. J. Bus. Innov. Res. – volume: 83 year: 2019 ident: b25 article-title: Self-adapting control parameters in particle swarm optimization publication-title: Appl. Soft. Comput. – volume: 8 start-page: 332 year: 2006 end-page: 344 ident: b29 article-title: Dynamic clustering using particle swarm optimization with application in image segmentation publication-title: Pattern Anal. Appl. – volume: 15 start-page: 533 year: 2011 end-page: 542 ident: b39 article-title: An application of particle swarm optimization algorithm to clustering analysis publication-title: Soft Comput. – volume: 7 start-page: 31883 year: 2019 end-page: 31902 ident: b16 article-title: Survey of state-of-the-art mixed data clustering algorithms publication-title: IEEE Access – volume: 46 start-page: 243 year: 2013 end-page: 256 ident: b43 article-title: An extensive comparative study of cluster validity indices publication-title: Pattern Recognit. – volume: 84 year: 2019 ident: b9 article-title: Improving K-means clustering with enhanced firefly algorithms publication-title: Appl. Soft. Comput. – volume: 38 start-page: 184 year: 2011 end-page: 192 ident: b31 article-title: A self-adaptive embedded chaotic particle swarm optimization for parameters selection of Wv-SVM publication-title: Expert Syst. Appl. – volume: 57 start-page: 331 year: 2018 end-page: 357 ident: b10 article-title: Automatic clustering using an improved artificial bee colony optimization for customer segmentation publication-title: Knowl. Inf. Syst. – volume: 393 start-page: 234 year: 2020 end-page: 244 ident: b23 article-title: An intelligent clustering algorithm for high-dimensional multiview data in big data applications publication-title: Neurocomputing – volume: 24 start-page: 9943 year: 2020 end-page: 9964 ident: b6 article-title: Application of gene expression programming and sensitivity analyses in analyzing effective parameters in gastric cancer tumor size and location publication-title: Soft Comput. – volume: 83 start-page: 98 year: 2012 end-page: 109 ident: b30 article-title: A novel chaotic particle swarm optimization based fuzzy clustering algorithm publication-title: Neurocomputing – volume: 140 year: 2020 ident: b5 article-title: Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches publication-title: Chaos Solitons Fractals – volume: 122 start-page: 2587 year: 2019 end-page: 2602 ident: b45 article-title: Regional difference analyzing and prediction model building for Chinese wine consumers’ sensory preference publication-title: Br. Food J. – volume: 18 start-page: 497 year: 2020 end-page: 510 ident: b12 article-title: A study on e-commerce customer segmentation management based on improved K-means algorithm publication-title: Inf. Syst. E-Bus. Manag. – volume: 48 start-page: 584 year: 2016 end-page: 596 ident: b27 article-title: Particle swarm optimization using dynamic tournament topology publication-title: Appl. Soft. Comput. – volume: 31 start-page: 8661 year: 2019 end-page: 8680 ident: b47 article-title: Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches publication-title: Neural Comput. Appl. – volume: 70 start-page: 590 year: 2018 end-page: 603 ident: b8 article-title: A modified bee colony optimization (MBCO) and its hybridization with k-means for an application to data clustering publication-title: Appl. Soft. Comput. – volume: 71 start-page: 375 year: 2017 end-page: 386 ident: b20 article-title: Fast density clustering strategies based on the K-means algorithm publication-title: Pattern Recognit. – volume: 43 year: 2018 ident: b19 article-title: An equi-biased k-prototypes algorithm for clustering mixed-type data publication-title: Sādhanā – volume: 43 start-page: 278 year: 2018 end-page: 284 ident: b3 article-title: From foodies to cherry-pickers: A clustered-based segmentation of specialty food retail customers publication-title: J. Retail. Consum. Serv. – volume: 21 start-page: 5081 year: 2017 end-page: 5090 ident: b32 article-title: Opposition-based particle swarm optimization with adaptive mutation strategy publication-title: Soft Comput. – volume: 36 start-page: 3336 year: 2009 end-page: 3341 ident: b22 article-title: A simple and fast algorithm for K-medoids clustering publication-title: Expert Syst. Appl. – volume: 35 start-page: 629 year: 2015 end-page: 636 ident: b26 article-title: Improved particle swarm optimization algorithm and its application in text feature selection publication-title: Appl. Soft. Comput. – volume: 454-455 start-page: 128 year: 2018 end-page: 143 ident: b46 article-title: Personalized restaurant recommendation method combining group correlations and customer preferences publication-title: Inf. Sci. – volume: 41 start-page: 1924 year: 2008 end-page: 1938 ident: b15 article-title: Mean shift spectral clustering publication-title: Pattern Recognit. – volume: 36 start-page: 9847 year: 2009 end-page: 9852 ident: b48 article-title: An efficient hybrid data clustering method based on K-harmonic means and particle swarm optimization publication-title: Expert Syst. Appl. – volume: 23 start-page: 333 year: 2014 end-page: 345 ident: b40 article-title: A novel particle swarm optimization algorithm with levy flight publication-title: Appl. Soft. Comput. – volume: 27 start-page: 857 year: 1971 end-page: 874 ident: b50 article-title: A general coefficient of similarity and some of its properties publication-title: Biometrics – volume: 38 start-page: 6159 year: 2020 end-page: 6173 ident: b4 article-title: The impact of big data market segmentation using data mining and clustering techniques publication-title: J. Intell. Fuzzy Syst. – year: 2016 ident: b17 article-title: An improved mixed-type data based kernel clustering algorithm publication-title: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD, 2016 – volume: 48 start-page: 1041 year: 2018 end-page: 1053 ident: b41 article-title: An improved publication-title: Appl. Intell. – volume: vol. 1274 start-page: 20 year: 2020 end-page: 32 ident: b51 article-title: An optimized hybrid clustering algorithm for mixed data: application to customer segmentation of table grapes in China publication-title: 2020 10th International Conference on Computer Engineering and Networks, CENet, 2020. Xi’an, China, 16-18 October – volume: 31 start-page: 651 year: 2010 end-page: 666 ident: b14 article-title: Data clustering: 50 years beyond K-means publication-title: Pattern Recognit. Lett. – volume: 28 start-page: 138 year: 2015 end-page: 149 ident: b34 article-title: A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques publication-title: Appl. Soft. Comput. – volume: 163 start-page: 358 year: 2019 end-page: 375 ident: b11 article-title: Evidential reasoning approach with multiple kinds of attributes and entropy-based weight assignment publication-title: Knowl.-Based Syst. – volume: 545 start-page: 344 year: 2021 end-page: 362 ident: b21 article-title: Near-optimal large-scale k-medoids clustering publication-title: Inform. Sci. – volume: 101 start-page: 345 year: 2015 end-page: 376 ident: b38 article-title: Random drift particle swarm optimization algorithm: convergence analysis and parameter selection publication-title: Mach. Learn. – volume: 158 start-page: 109 year: 2015 end-page: 116 ident: b37 article-title: Automatic test data generation based on reduced adaptive particle swarm optimization algorithm publication-title: Neurocomputing – volume: 32 start-page: 1062 year: 2011 end-page: 1069 ident: b18 article-title: A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets publication-title: Pattern Recognit. Lett. – volume: 293 start-page: 370 year: 2015 end-page: 382 ident: b36 article-title: Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems publication-title: Inform. Sci. – volume: 47 start-page: 547 year: 2009 end-page: 553 ident: b42 article-title: Modeling wine preferences by data mining from physicochemical properties publication-title: Decis. Support Syst. – volume: 60 start-page: 752 year: 2017 end-page: 762 ident: b44 article-title: Clustering retail products based on customer behaviour publication-title: Appl. Soft. Comput. – volume: 10 start-page: 183 year: 2010 end-page: 197 ident: b13 article-title: An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis publication-title: Appl. Soft. Comput. – start-page: 1942 year: 1995 end-page: 1948 ident: b24 article-title: Particle swarm optimization publication-title: 1995 IEEE International Conference on Neural Networks, 2011. Perth, Australia, November 27- December. 4 – year: 2020 ident: b7 article-title: Coviddeep: SARS-CoV-2/COVID-19 test based on wearable medical sensors and efficient neural networks publication-title: arXiv – volume: 56 start-page: 748 year: 2016 end-page: 762 ident: b35 article-title: Integrated particle swarm optimization algorithm based obstacle avoidance control design for home service robot publication-title: Comput. Electr. Eng. – volume: 22 start-page: 221 year: 2019 end-page: 232 ident: b1 article-title: Document representation based on probabilistic word clustering in customer-voice classification publication-title: Pattern Anal. Appl. – volume: 84 year: 2019 ident: 10.1016/j.asoc.2021.107924_b9 article-title: Improving K-means clustering with enhanced firefly algorithms publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2019.105763 – volume: 32 start-page: 1062 issue: 7 year: 2011 ident: 10.1016/j.asoc.2021.107924_b18 article-title: A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2011.02.017 – volume: 158 start-page: 109 year: 2015 ident: 10.1016/j.asoc.2021.107924_b37 article-title: Automatic test data generation based on reduced adaptive particle swarm optimization algorithm publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.01.062 – volume: 43 start-page: 278 year: 2018 ident: 10.1016/j.asoc.2021.107924_b3 article-title: From foodies to cherry-pickers: A clustered-based segmentation of specialty food retail customers publication-title: J. Retail. Consum. Serv. doi: 10.1016/j.jretconser.2018.04.010 – volume: 122 start-page: 2587 issue: 8 year: 2019 ident: 10.1016/j.asoc.2021.107924_b45 article-title: Regional difference analyzing and prediction model building for Chinese wine consumers’ sensory preference publication-title: Br. Food J. doi: 10.1108/BFJ-06-2019-0465 – volume: 57 start-page: 331 issue: 2 year: 2018 ident: 10.1016/j.asoc.2021.107924_b10 article-title: Automatic clustering using an improved artificial bee colony optimization for customer segmentation publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-018-1162-5 – volume: 48 start-page: 1041 issue: 4 year: 2018 ident: 10.1016/j.asoc.2021.107924_b41 article-title: An improved ν-twin bounded support vector machine publication-title: Appl. Intell. doi: 10.1007/s10489-017-0984-2 – year: 2016 ident: 10.1016/j.asoc.2021.107924_b17 article-title: An improved mixed-type data based kernel clustering algorithm – volume: 23 start-page: 333 year: 2014 ident: 10.1016/j.asoc.2021.107924_b40 article-title: A novel particle swarm optimization algorithm with levy flight publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2014.06.034 – volume: 36 start-page: 3336 issue: 2 year: 2009 ident: 10.1016/j.asoc.2021.107924_b22 article-title: A simple and fast algorithm for K-medoids clustering publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.01.039 – volume: 31 start-page: 651 issue: 8 year: 2010 ident: 10.1016/j.asoc.2021.107924_b14 article-title: Data clustering: 50 years beyond K-means publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2009.09.011 – volume: 43 issue: 3 year: 2018 ident: 10.1016/j.asoc.2021.107924_b19 article-title: An equi-biased k-prototypes algorithm for clustering mixed-type data publication-title: Sādhanā doi: 10.1007/s12046-018-0823-0 – volume: 60 start-page: 752 year: 2017 ident: 10.1016/j.asoc.2021.107924_b44 article-title: Clustering retail products based on customer behaviour publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2017.02.004 – volume: 36 start-page: 687 issue: 3 year: 2016 ident: 10.1016/j.asoc.2021.107924_b33 article-title: Improved particle swarm optimization algorithm based on Gaussian disturbance and natural selection publication-title: J. Comput. Appl. – year: 2020 ident: 10.1016/j.asoc.2021.107924_b7 article-title: Coviddeep: SARS-CoV-2/COVID-19 test based on wearable medical sensors and efficient neural networks publication-title: arXiv – volume: 21 start-page: 5081 issue: 17 year: 2017 ident: 10.1016/j.asoc.2021.107924_b32 article-title: Opposition-based particle swarm optimization with adaptive mutation strategy publication-title: Soft Comput. doi: 10.1007/s00500-016-2102-5 – volume: 35 start-page: 629 year: 2015 ident: 10.1016/j.asoc.2021.107924_b26 article-title: Improved particle swarm optimization algorithm and its application in text feature selection publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2015.07.005 – volume: vol. 1274 start-page: 20 year: 2020 ident: 10.1016/j.asoc.2021.107924_b51 article-title: An optimized hybrid clustering algorithm for mixed data: application to customer segmentation of table grapes in China – volume: 24 start-page: 9943 issue: 13 year: 2020 ident: 10.1016/j.asoc.2021.107924_b6 article-title: Application of gene expression programming and sensitivity analyses in analyzing effective parameters in gastric cancer tumor size and location publication-title: Soft Comput. doi: 10.1007/s00500-019-04507-0 – volume: 163 start-page: 358 year: 2019 ident: 10.1016/j.asoc.2021.107924_b11 article-title: Evidential reasoning approach with multiple kinds of attributes and entropy-based weight assignment publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2018.08.037 – volume: 48 start-page: 584 year: 2016 ident: 10.1016/j.asoc.2021.107924_b27 article-title: Particle swarm optimization using dynamic tournament topology publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2016.07.041 – volume: 7 start-page: 31883 year: 2019 ident: 10.1016/j.asoc.2021.107924_b16 article-title: Survey of state-of-the-art mixed data clustering algorithms publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2903568 – volume: 27 start-page: 857 year: 1971 ident: 10.1016/j.asoc.2021.107924_b50 article-title: A general coefficient of similarity and some of its properties publication-title: Biometrics doi: 10.2307/2528823 – volume: 70 start-page: 590 year: 2018 ident: 10.1016/j.asoc.2021.107924_b8 article-title: A modified bee colony optimization (MBCO) and its hybridization with k-means for an application to data clustering publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2018.05.045 – volume: 83 year: 2019 ident: 10.1016/j.asoc.2021.107924_b25 article-title: Self-adapting control parameters in particle swarm optimization publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2019.105653 – volume: 29 start-page: 986 issue: 7 year: 2008 ident: 10.1016/j.asoc.2021.107924_b49 article-title: Distance functions for categorical and mixed variables publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2008.01.021 – volume: 41 start-page: 1924 issue: 6 year: 2008 ident: 10.1016/j.asoc.2021.107924_b15 article-title: Mean shift spectral clustering publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2007.09.009 – volume: 293 start-page: 370 year: 2015 ident: 10.1016/j.asoc.2021.107924_b36 article-title: Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems publication-title: Inform. Sci. doi: 10.1016/j.ins.2014.09.030 – volume: 140 year: 2020 ident: 10.1016/j.asoc.2021.107924_b5 article-title: Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches publication-title: Chaos Solitons Fractals doi: 10.1016/j.chaos.2020.110170 – volume: 18 start-page: 497 issue: 4 year: 2020 ident: 10.1016/j.asoc.2021.107924_b12 article-title: A study on e-commerce customer segmentation management based on improved K-means algorithm publication-title: Inf. Syst. E-Bus. Manag. doi: 10.1007/s10257-018-0381-3 – volume: 56 start-page: 748 year: 2016 ident: 10.1016/j.asoc.2021.107924_b35 article-title: Integrated particle swarm optimization algorithm based obstacle avoidance control design for home service robot publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2015.05.019 – volume: 47 start-page: 547 issue: 4 year: 2009 ident: 10.1016/j.asoc.2021.107924_b42 article-title: Modeling wine preferences by data mining from physicochemical properties publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2009.05.016 – volume: 67 start-page: 172 year: 2018 ident: 10.1016/j.asoc.2021.107924_b28 article-title: An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2018.03.011 – volume: 393 start-page: 234 year: 2020 ident: 10.1016/j.asoc.2021.107924_b23 article-title: An intelligent clustering algorithm for high-dimensional multiview data in big data applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.12.093 – volume: 454-455 start-page: 128 year: 2018 ident: 10.1016/j.asoc.2021.107924_b46 article-title: Personalized restaurant recommendation method combining group correlations and customer preferences publication-title: Inf. Sci. doi: 10.1016/j.ins.2018.04.061 – volume: 101 start-page: 345 issue: 1–3 year: 2015 ident: 10.1016/j.asoc.2021.107924_b38 article-title: Random drift particle swarm optimization algorithm: convergence analysis and parameter selection publication-title: Mach. Learn. doi: 10.1007/s10994-015-5522-z – volume: 83 start-page: 98 year: 2012 ident: 10.1016/j.asoc.2021.107924_b30 article-title: A novel chaotic particle swarm optimization based fuzzy clustering algorithm publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.12.009 – volume: 22 start-page: 221 issue: 1 year: 2019 ident: 10.1016/j.asoc.2021.107924_b1 article-title: Document representation based on probabilistic word clustering in customer-voice classification publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-018-00772-1 – volume: 15 start-page: 533 issue: 3 year: 2011 ident: 10.1016/j.asoc.2021.107924_b39 article-title: An application of particle swarm optimization algorithm to clustering analysis publication-title: Soft Comput. doi: 10.1007/s00500-009-0539-5 – volume: 38 start-page: 6159 issue: 5 year: 2020 ident: 10.1016/j.asoc.2021.107924_b4 article-title: The impact of big data market segmentation using data mining and clustering techniques publication-title: J. Intell. Fuzzy Syst. – volume: 36 start-page: 9847 issue: 6 year: 2009 ident: 10.1016/j.asoc.2021.107924_b48 article-title: An efficient hybrid data clustering method based on K-harmonic means and particle swarm optimization publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2009.02.003 – volume: 38 start-page: 184 issue: 1 year: 2011 ident: 10.1016/j.asoc.2021.107924_b31 article-title: A self-adaptive embedded chaotic particle swarm optimization for parameters selection of Wv-SVM publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.06.038 – volume: 8 start-page: 168 issue: 2 year: 2014 ident: 10.1016/j.asoc.2021.107924_b2 article-title: Customer-oriented benefit segmentation: an integrated approach publication-title: Int. J. Bus. Innov. Res. doi: 10.1504/IJBIR.2014.059532 – volume: 71 start-page: 375 year: 2017 ident: 10.1016/j.asoc.2021.107924_b20 article-title: Fast density clustering strategies based on the K-means algorithm publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.06.023 – volume: 10 start-page: 183 issue: 1 year: 2010 ident: 10.1016/j.asoc.2021.107924_b13 article-title: An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2009.07.001 – volume: 28 start-page: 138 year: 2015 ident: 10.1016/j.asoc.2021.107924_b34 article-title: A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2014.11.018 – volume: 545 start-page: 344 year: 2021 ident: 10.1016/j.asoc.2021.107924_b21 article-title: Near-optimal large-scale k-medoids clustering publication-title: Inform. Sci. doi: 10.1016/j.ins.2020.08.121 – volume: 31 start-page: 8661 issue: 12 year: 2019 ident: 10.1016/j.asoc.2021.107924_b47 article-title: Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches publication-title: Neural Comput. Appl. doi: 10.1007/s00521-019-04417-0 – volume: 46 start-page: 243 issue: 1 year: 2013 ident: 10.1016/j.asoc.2021.107924_b43 article-title: An extensive comparative study of cluster validity indices publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2012.07.021 – volume: 8 start-page: 332 issue: 4 year: 2006 ident: 10.1016/j.asoc.2021.107924_b29 article-title: Dynamic clustering using particle swarm optimization with application in image segmentation publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-005-0015-5 – start-page: 1942 year: 1995 ident: 10.1016/j.asoc.2021.107924_b24 article-title: Particle swarm optimization |
| SSID | ssj0016928 |
| Score | 2.5705802 |
| Snippet | The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 107924 |
| SubjectTerms | Adaptive parameter learning Customer segmentation K-means clustering algorithm Mixed data Particle swarm optimization algorithm |
| Title | Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm |
| URI | https://dx.doi.org/10.1016/j.asoc.2021.107924 |
| Volume | 113 |
| WOSCitedRecordID | wos000724862700008&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-9681 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0016928 issn: 1568-4946 databaseCode: AIEXJ dateStart: 20010601 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLbQxgMv3BFjgPzAW-QpTZw4fpzKEDdNSAypPEWO7WydmrRqE9j-Pce3NOrQBEi8RNFpbFfnfDn-6p4LQm84p0C7bZl9lRMqYkrEpC5IJdJCMkljpW2d2c_s9LSYzfgXHzq0se0EWNsWV1d89V9NDTIwtkmd_QtzD5OCAO7B6HAFs8P1jww_7YHPNaZIsz5vfGZRG_X2TOATaTTsTZFc9KZAQkhQNORTKLGyYUQrP2e0-SnWTbQEaeOTNSOxOF-u591FM-a0gchuwKPbEPW-C_uhifSx4QLf-wFA04veSGZzAXuSGMUBu6PYt8vtWNCrixeGz67DnP6IIpnshHvczJ1xrjYvCOX-AFI7WcESwnPXxGXwzy5Z9Yavd8cOl0cCYHxklgUR4y4je6eG9lezmFkrMbWMqNmz9xOWcXCD-8cfTmYfhz-ecm7b8Q5fzudZuZDA3ZV-z2VG_OTsIbrvf1jgY2e8R-iObh-jB6FpB_Y-_AnSAR94jA9s8YE9PvAWHxjwgQEfOOADB3xgiw88xgce8PEUfXt3cjZ9T3yvDSLTOO5IRblMa1YB_U5jLrQoVFKnjArBNYWXt2ZpHdfAeICfpiqWhvYqrrJUJ1xW8OgztNcuW_0cYSkmTDMuRMZz2IYZr5IKdF3XmcoyJesDNAlaK6UvRG_6oSzKEHF4WRpNl0bTpdP0AYqGMStXhuXWp7NgjNITSUcQS8DOLeNe_OO4Q3RvC_uXaK9b9_oVuit_dPPN-rWH2C9MaJsk |
| linkProvider | Elsevier |
| 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=Customer+segmentation+using+K-means+clustering+and+the+adaptive+particle+swarm+optimization+algorithm&rft.jtitle=Applied+soft+computing&rft.au=Li%2C+Yue&rft.au=Chu%2C+Xiaoquan&rft.au=Tian%2C+Dong&rft.au=Feng%2C+Jianying&rft.date=2021-12-01&rft.pub=Elsevier+B.V&rft.issn=1568-4946&rft.eissn=1872-9681&rft.volume=113&rft_id=info:doi/10.1016%2Fj.asoc.2021.107924&rft.externalDocID=S1568494621008462 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1568-4946&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1568-4946&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1568-4946&client=summon |