Fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging
Density peaks clustering (DPC) has had an impact in many fields, as it can quickly select centers and effectively process complex data. However, it also has low operational efficiency and a “Domino” effect. To solve these defects, we propose a fast density peaks clustering algorithm based on improve...
Uložené v:
| Vydané v: | Information sciences Ročník 647; s. 119470 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Inc
01.11.2023
|
| Predmet: | |
| ISSN: | 0020-0255 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Density peaks clustering (DPC) has had an impact in many fields, as it can quickly select centers and effectively process complex data. However, it also has low operational efficiency and a “Domino” effect. To solve these defects, we propose a fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging (KS-FDPC). The new algorithm adopts a partitioning-merging strategy. By dividing the data into multiple sub-clusters, the impact range of high-density points on subsequent allocation points can be reduced. And the fast nearest neighbors search improves the speed of DPC. In the experiment, KS-FDPC is used to compare with eight improved DPC algorithms on eight synthetic data and eight UCI data. The results indicate that the overall clustering performance of KS-FDPC is superior to other algorithms. Moreover, KS-FDPC runs faster than other algorithms. Therefore, KS-FDPC is an effective improvement of DPC. |
|---|---|
| AbstractList | Density peaks clustering (DPC) has had an impact in many fields, as it can quickly select centers and effectively process complex data. However, it also has low operational efficiency and a “Domino” effect. To solve these defects, we propose a fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging (KS-FDPC). The new algorithm adopts a partitioning-merging strategy. By dividing the data into multiple sub-clusters, the impact range of high-density points on subsequent allocation points can be reduced. And the fast nearest neighbors search improves the speed of DPC. In the experiment, KS-FDPC is used to compare with eight improved DPC algorithms on eight synthetic data and eight UCI data. The results indicate that the overall clustering performance of KS-FDPC is superior to other algorithms. Moreover, KS-FDPC runs faster than other algorithms. Therefore, KS-FDPC is an effective improvement of DPC. |
| ArticleNumber | 119470 |
| Author | Xu, Xiao Li, Chao Ding, Ling Ding, Shifei Hou, Haiwei |
| Author_xml | – sequence: 1 givenname: Chao surname: Li fullname: Li, Chao organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 2 givenname: Shifei surname: Ding fullname: Ding, Shifei email: dingsf@cumt.edu.cn organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 3 givenname: Xiao surname: Xu fullname: Xu, Xiao organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 4 givenname: Haiwei orcidid: 0000-0003-1580-918X surname: Hou fullname: Hou, Haiwei organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 5 givenname: Ling orcidid: 0000-0002-3208-2528 surname: Ding fullname: Ding, Ling organization: College of Intelligence and Computing, Tianjin University, Tianjin 300350, China |
| BookMark | eNp9kL1OwzAURj0UibbwAGx-gQTb-XEiJlRRQFRigdlynOvUbWJXtlOpb0-qdmLo9N3lHOmeBZpZZwGhJ0pSSmj5vEuNDSkjLEsprXNOZmhOCCMJYUVxjxYh7AghOS_LOdqvZYi4BRtMPOEDyH3Aqh9DBG9sh2XfOW_idsCNDNBiZ7EZDt4dp3sY4yh7_JVYkB5CnNZ028Z5LG2Lw9gkVxEewHeT7QHdadkHeLzuEv2u335WH8nm-_1z9bpJFKt5TNq6oXVWaZBUA5N5rVkOddE0tGiqNuctVZoQylVe8YIDcF3wrGoYZBnluoRsifjFq7wLwYMWykQZjbPRS9MLSsS5k9iJqZM4dxKXThNJ_5EHbwbpTzeZlwsD00tHA14EZcAqaI0HFUXrzA36D5lShys |
| CitedBy_id | crossref_primary_10_1016_j_ins_2024_120858 crossref_primary_10_1049_cit2_70050 crossref_primary_10_1016_j_patcog_2025_112357 crossref_primary_10_1016_j_eswa_2024_125934 crossref_primary_10_3389_fams_2025_1598165 crossref_primary_10_1109_TKDE_2025_3589794 crossref_primary_10_1038_s41598_024_74016_0 crossref_primary_10_1016_j_ins_2024_121211 crossref_primary_10_1016_j_ins_2024_120685 crossref_primary_10_1016_j_ins_2024_121256 crossref_primary_10_1016_j_enbuild_2024_115175 crossref_primary_10_1109_ACCESS_2025_3563990 crossref_primary_10_1109_TPAMI_2025_3535743 crossref_primary_10_1016_j_patcog_2024_110366 crossref_primary_10_1109_TNNLS_2023_3329720 crossref_primary_10_1016_j_patcog_2025_111953 crossref_primary_10_1016_j_patcog_2025_112205 crossref_primary_10_1080_10095020_2024_2343323 crossref_primary_10_1007_s10586_024_04592_3 crossref_primary_10_1007_s11227_024_06559_y crossref_primary_10_1109_ACCESS_2024_3518497 crossref_primary_10_1016_j_engappai_2025_111981 crossref_primary_10_1016_j_asoc_2024_111779 crossref_primary_10_1016_j_ins_2024_120114 crossref_primary_10_1016_j_ins_2024_120731 crossref_primary_10_1016_j_ins_2024_120811 crossref_primary_10_1016_j_ins_2024_121526 |
| Cites_doi | 10.1109/TLA.2021.9475870 10.1002/cpe.5567 10.1109/TII.2016.2628747 10.1155/2020/1731075 10.1016/j.ins.2022.12.078 10.1109/TNNLS.2018.2853710 10.1016/j.knosys.2016.02.001 10.1016/j.neucom.2019.07.048 10.1109/ICCSP48568.2020.9182211 10.1109/TKDE.2019.2954133 10.1109/TKDE.2020.3034611 10.1109/ACCESS.2019.2927308 10.1109/ACCESS.2019.2912332 10.1109/TCYB.2019.2902603 10.1111/j.2517-6161.1977.tb01600.x 10.1109/TKDE.2019.2930056 10.1016/j.ins.2020.11.050 10.1016/j.ins.2016.03.011 10.1109/BigComp.2018.00084 10.1007/s13042-016-0603-2 10.1016/j.knosys.2018.05.034 10.1016/j.patrec.2019.10.019 10.1126/science.1242072 10.1109/TIE.2018.2856200 10.1109/TVCG.2020.3030347 10.1109/IWOBI47054.2019.9114411 10.1016/j.ins.2018.03.031 10.1109/ACCESS.2019.2904254 10.1016/j.ins.2022.11.091 10.1109/TFUZZ.2020.2985004 10.1109/ICECA52323.2021.9675894 10.1007/s00521-020-04754-5 10.1109/ICDM51629.2021.00074 10.1007/s10898-019-00779-w 10.1016/j.patcog.2022.109238 10.1007/s13042-019-01031-3 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier Inc. |
| Copyright_xml | – notice: 2023 Elsevier Inc. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.ins.2023.119470 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Library & Information Science |
| ExternalDocumentID | 10_1016_j_ins_2023_119470 S0020025523010551 |
| GroupedDBID | --K --M --Z -~X .DC .~1 0R~ 1B1 1OL 1RT 1~. 1~5 29I 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AABNK AACTN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXKI AAXUO AAYFN ABAOU ABBOA ABEFU ABFNM ABJNI ABMAC ABTAH ABUCO ABXDB ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADGUI ADJOM ADMUD ADTZH ADVLN AEBSH AECPX AEKER AENEX AFFNX AFJKZ AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM LG9 LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SST SSV SSW SSZ T5K TN5 TWZ UHS WH7 WUQ XPP YYP ZMT ZY4 ~02 ~G- 77I 9DU AATTM AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c297t-d9b1938fea1fe2a49f24e95bb15b8d47d1cf0017c48757ee7f5738b2e3317f6e3 |
| ISICitedReferencesCount | 34 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001062207400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0020-0255 |
| IngestDate | Sat Nov 29 02:44:08 EST 2025 Tue Nov 18 21:38:59 EST 2025 Sat Nov 02 16:00:15 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Sparse matrix Sub-cluster merging Kd-tree Improved mutual K-nearest-neighbor Density peaks clustering |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c297t-d9b1938fea1fe2a49f24e95bb15b8d47d1cf0017c48757ee7f5738b2e3317f6e3 |
| ORCID | 0000-0002-3208-2528 0000-0003-1580-918X |
| ParticipantIDs | crossref_citationtrail_10_1016_j_ins_2023_119470 crossref_primary_10_1016_j_ins_2023_119470 elsevier_sciencedirect_doi_10_1016_j_ins_2023_119470 |
| PublicationCentury | 2000 |
| PublicationDate | November 2023 2023-11-00 |
| PublicationDateYYYYMMDD | 2023-11-01 |
| PublicationDate_xml | – month: 11 year: 2023 text: November 2023 |
| PublicationDecade | 2020 |
| PublicationTitle | Information sciences |
| PublicationYear | 2023 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Sieranoja, Fränti (b0165) 2019; 128 Ding, Du, Xu, Shi, Wang, Li (b0100) 2023; 624 International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2021: 1376–1379. Wang, Wang, Zhang, Pang, Miao, Tan, Zhou (b0180) 2020; 32 Xu, Ding, Du, Xue (b0075) 2018; 9 Ouyang, Pedrycz, Pizzi (b0065) 2021; 51 Wu, Lee, Isokawa, Yao, Xia (b0185) 2019; 7 Rodriguez, Laio (b0070) 2014; 344 Cheng, Zhu, Huang, Wu, Yang (b0190) 2019; 30 Tobin J, Zhang M. DCF: An Efficient and Robust Density-Based Clustering Method. 2021 IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand, 2021: 629–638. Qiu, Li (b0150) 2022 Yao, Ge (b0030) 2019; 66 Zhao, Tang, Fan, Li, Xu (b0080) 2020; 32 Yu, Liu, Guo, Liu, Yao (b0120) 2019; 7 Pister, Buono, Fekete, Plaisant, Valdivia (b0015) 2021; 27 Xu, Ding, Shi (b0125) 2018; 158 Chen, Yu (b0105) 2021; 33 Liu, Wang, Yu (b0085) 2018; 450 Fuentealba, López, Ponce (b0005) 2021; 19 Zhu, Zhang, Wen, Liu (b0035) 2019; 363 Wu, Wilamowski (b0160) 2017; 13 Jebari S, Smiti A, Louati A. AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach. 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), Budapest, Hungary, 2019: 1–6. Xu, Ding, Wang, Wang, Jia (b0130) 2021; 554 Lu, Zhao, Tan, Wang (b0170) 2022; 34 Xu X, Ding S, Sun T. A Fast Density Peaks Clustering Algorithm Based on Pre-Screening. 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, 2018: 513–516. Du, Ding, Jia (b0115) 2016; 99 Macqueen J. Some methods for classification and analysis of multivariate observations. Proceedings of the 5 Wang, Wang, Zhou (b0175) 2023; 621 Berkeley symposium on mathematical statistics and probability, San Francisco, USA, 1967: 281–297. Fan, Jia, Ge (b0110) 2020; 11 Cheng, Zhu, Huang, Wu, Yang (b0155) 2021; 33 Xie, Gao, Xie, Liu, Grant (b0095) 2016; 354 Rudrappa G, Vijapur N. Cloud Classification using K-Means Clustering and Content based Image Retrieval Technique. 2020 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2020: 0700-0704. Bian, Chung, Wang (b0135) 2021; 29 Peng M, Hu B, Tan Z. Hierarchical Structure of E-commerce Big Data Clustering Based on Hadoop Platform. 2021 5 Vinh, Epps, Bailey (b0145) 2010; 11 Dempster, Laird, Rubin (b0055) 1977; 39 Sun, Bao, Ci, Zheng, Guo, Luo (b0140) 2019; 7 Li, Xu, Zhang, Zou (b0025) 2020; 76 Ding, Li, Xu, Ding, Zhang, Guo, Shi (b0060) 2023; 136 Diao, Dai, An, Li, Feng, Pan (b0090) 2020; 2020 Xie (10.1016/j.ins.2023.119470_b0095) 2016; 354 Xu (10.1016/j.ins.2023.119470_b0130) 2021; 554 Wu (10.1016/j.ins.2023.119470_b0160) 2017; 13 Ouyang (10.1016/j.ins.2023.119470_b0065) 2021; 51 Wang (10.1016/j.ins.2023.119470_b0175) 2023; 621 Liu (10.1016/j.ins.2023.119470_b0085) 2018; 450 Wang (10.1016/j.ins.2023.119470_b0180) 2020; 32 Yu (10.1016/j.ins.2023.119470_b0120) 2019; 7 Sun (10.1016/j.ins.2023.119470_b0140) 2019; 7 10.1016/j.ins.2023.119470_b0040 Xu (10.1016/j.ins.2023.119470_b0125) 2018; 158 10.1016/j.ins.2023.119470_b0020 10.1016/j.ins.2023.119470_b0045 Dempster (10.1016/j.ins.2023.119470_b0055) 1977; 39 Lu (10.1016/j.ins.2023.119470_b0170) 2022; 34 Pister (10.1016/j.ins.2023.119470_b0015) 2021; 27 Diao (10.1016/j.ins.2023.119470_b0090) 2020; 2020 Yao (10.1016/j.ins.2023.119470_b0030) 2019; 66 Ding (10.1016/j.ins.2023.119470_b0100) 2023; 624 Bian (10.1016/j.ins.2023.119470_b0135) 2021; 29 Li (10.1016/j.ins.2023.119470_b0025) 2020; 76 Vinh (10.1016/j.ins.2023.119470_b0145) 2010; 11 Sieranoja (10.1016/j.ins.2023.119470_b0165) 2019; 128 Ding (10.1016/j.ins.2023.119470_b0060) 2023; 136 Du (10.1016/j.ins.2023.119470_b0115) 2016; 99 Cheng (10.1016/j.ins.2023.119470_b0155) 2021; 33 Zhu (10.1016/j.ins.2023.119470_b0035) 2019; 363 Chen (10.1016/j.ins.2023.119470_b0105) 2021; 33 10.1016/j.ins.2023.119470_b0050 10.1016/j.ins.2023.119470_b0195 10.1016/j.ins.2023.119470_b0010 Fan (10.1016/j.ins.2023.119470_b0110) 2020; 11 Zhao (10.1016/j.ins.2023.119470_b0080) 2020; 32 Fuentealba (10.1016/j.ins.2023.119470_b0005) 2021; 19 Xu (10.1016/j.ins.2023.119470_b0075) 2018; 9 Wu (10.1016/j.ins.2023.119470_b0185) 2019; 7 Rodriguez (10.1016/j.ins.2023.119470_b0070) 2014; 344 Cheng (10.1016/j.ins.2023.119470_b0190) 2019; 30 Qiu (10.1016/j.ins.2023.119470_b0150) 2022 |
| References_xml | – volume: 19 start-page: 1391 year: 2021 end-page: 1399 ident: b0005 article-title: Effects on time and quality of short text clustering during real-time presentations publication-title: IEEE Lat. Am. Trans. – volume: 66 start-page: 3681 year: 2019 end-page: 3692 ident: b0030 article-title: Scalable semisupervised GMM for big data quality prediction in multimode processes publication-title: IEEE Trans. Ind. Electron. – volume: 34 start-page: 3714 year: 2022 end-page: 3726 ident: b0170 article-title: Distributed density peaks clustering revisited publication-title: IEEE Trans. Knowl. Data Eng. – volume: 29 start-page: 1725 year: 2021 end-page: 1738 ident: b0135 article-title: Fuzzy density peaks clustering publication-title: IEEE Trans. Fuzzy Syst. – reference: Jebari S, Smiti A, Louati A. AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach. 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), Budapest, Hungary, 2019: 1–6. – volume: 32 year: 2020 ident: b0080 article-title: Density peaks clustering based on circular partition and grid similarity publication-title: Concurr Comput: Practice Exp – volume: 136 year: 2023 ident: b0060 article-title: A sampling-based density peaks clustering algorithm for large-scale data publication-title: Pattern Recogn. – reference: Tobin J, Zhang M. DCF: An Efficient and Robust Density-Based Clustering Method. 2021 IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand, 2021: 629–638. – volume: 363 start-page: 149 year: 2019 end-page: 170 ident: b0035 article-title: Fast and stable clustering analysis based on Grid-mapping K-means algorithm and new clustering validity index publication-title: Neurocomputing – volume: 11 start-page: 2837 year: 2010 end-page: 2854 ident: b0145 article-title: Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance publication-title: J. Mach. Learn. Res. – volume: 344 start-page: 1492 year: 2014 end-page: 1496 ident: b0070 article-title: Clustering by fast search and find of density peaks publication-title: Science – reference: Rudrappa G, Vijapur N. Cloud Classification using K-Means Clustering and Content based Image Retrieval Technique. 2020 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2020: 0700-0704. – year: 2022 ident: b0150 article-title: Fast LDP-MST: an efficient density-peak-based clustering method for large-size datasets publication-title: IEEE Trans. Knowl. Data Eng. – volume: 33 start-page: 374 year: 2021 end-page: 387 ident: b0155 article-title: Clustering with local density peaks-based minimum spanning tree publication-title: IEEE Trans. Knowl. Data Eng. – volume: 13 start-page: 1620 year: 2017 end-page: 1628 ident: b0160 article-title: A fast density and grid based clustering method for data with arbitrary shapes and noise publication-title: IEEE Trans. Ind. Inf. – volume: 7 start-page: 89427 year: 2019 end-page: 89440 ident: b0140 article-title: Differential privacy-preserving density peaks clustering based on shared near neighbors similarity publication-title: IEEE Access – volume: 7 start-page: 34301 year: 2019 end-page: 34317 ident: b0120 article-title: Density peaks clustering based on weighted local density sequence and nearest neighbor assignment publication-title: IEEE Access – volume: 128 start-page: 551 year: 2019 end-page: 558 ident: b0165 article-title: Fast and general density peaks clustering publication-title: Pattern Recogn. Lett. – volume: 27 start-page: 1775 year: 2021 end-page: 1785 ident: b0015 article-title: Integrating prior knowledge in mixed-initiative social network clustering publication-title: IEEE Trans. Vis. Comput. Graph. – volume: 2020 start-page: 1 year: 2020 end-page: 17 ident: b0090 article-title: Clustering by detecting density peaks and assigning points by similarity-first search based on weighted K-nearest neighbors graph publication-title: Complexity – reference: Berkeley symposium on mathematical statistics and probability, San Francisco, USA, 1967: 281–297. – volume: 9 start-page: 743 year: 2018 end-page: 754 ident: b0075 article-title: DPCG: an efficient density peaks clustering algorithm based on grid publication-title: Int. J. Mach. Learn. Cybern. – volume: 33 start-page: 2310 year: 2021 end-page: 2321 ident: b0105 article-title: A domain adaptive density clustering algorithm for data with varying density distribution publication-title: IEEE Trans. Knowl. Data Eng. – volume: 99 start-page: 135 year: 2016 end-page: 145 ident: b0115 article-title: Study on density peaks clustering based on k-nearest neighbors and principal component analysis publication-title: Knowl.-Based Syst. – volume: 51 start-page: 3653 year: 2021 end-page: 3663 ident: b0065 article-title: Rule-based modeling with DBSCAN-based information granules publication-title: IEEE Trans. Cybern. – reference: International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2021: 1376–1379. – volume: 158 start-page: 65 year: 2018 end-page: 74 ident: b0125 article-title: An improved density peaks clustering algorithm with fast finding cluster centers publication-title: Knowl.-Based Syst. – reference: Xu X, Ding S, Sun T. A Fast Density Peaks Clustering Algorithm Based on Pre-Screening. 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, 2018: 513–516. – volume: 30 start-page: 985 year: 2019 end-page: 999 ident: b0190 article-title: A novel cluster validity index based on local cores publication-title: IEEE Trans. Neural Networks Learn. Syst. – volume: 11 start-page: 1179 year: 2020 end-page: 1195 ident: b0110 article-title: M publication-title: Int. J. Mach. Learn. Cybern. – volume: 7 start-page: 60684 year: 2019 end-page: 60696 ident: b0185 article-title: Efficient clustering method based on density peaks with symmetric neighborhood relationship publication-title: IEEE Access – volume: 354 start-page: 19 year: 2016 end-page: 40 ident: b0095 article-title: Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors publication-title: Inf. Sci. – volume: 554 start-page: 61 year: 2021 end-page: 83 ident: b0130 article-title: A fast density peaks clustering algorithm with sparse search publication-title: Inf. Sci. – volume: 624 start-page: 252 year: 2023 end-page: 276 ident: b0100 article-title: An improved density peaks clustering algorithm based on natural neighbor with a merging strategy publication-title: Inf. Sci. – volume: 621 start-page: 627 year: 2023 end-page: 651 ident: b0175 article-title: VDPC: variational density peaks clustering algorithm publication-title: Inf. Sci. – volume: 76 start-page: 695 year: 2020 end-page: 708 ident: b0025 article-title: The seeding algorithms for spherical k-means clustering publication-title: J. Glob. Optim. – volume: 450 start-page: 200 year: 2018 end-page: 226 ident: b0085 article-title: Shared-nearest-neighbor-based clustering by fast search and find of density peaks publication-title: Inf. Sci. – volume: 32 start-page: 13465 year: 2020 end-page: 13478 ident: b0180 article-title: McDPC: multi-center density peak clustering publication-title: Neural Comput. & Applic. – reference: Peng M, Hu B, Tan Z. Hierarchical Structure of E-commerce Big Data Clustering Based on Hadoop Platform. 2021 5 – reference: Macqueen J. Some methods for classification and analysis of multivariate observations. Proceedings of the 5 – volume: 39 start-page: 1 year: 1977 end-page: 22 ident: b0055 article-title: Maximum likelihood from incomplete data via the EM algorithm publication-title: J. R. Stat. Soc. Ser. B – volume: 19 start-page: 1391 issue: 8 year: 2021 ident: 10.1016/j.ins.2023.119470_b0005 article-title: Effects on time and quality of short text clustering during real-time presentations publication-title: IEEE Lat. Am. Trans. doi: 10.1109/TLA.2021.9475870 – volume: 32 issue: 7 year: 2020 ident: 10.1016/j.ins.2023.119470_b0080 article-title: Density peaks clustering based on circular partition and grid similarity publication-title: Concurr Comput: Practice Exp doi: 10.1002/cpe.5567 – volume: 13 start-page: 1620 issue: 4 year: 2017 ident: 10.1016/j.ins.2023.119470_b0160 article-title: A fast density and grid based clustering method for data with arbitrary shapes and noise publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2016.2628747 – volume: 2020 start-page: 1 year: 2020 ident: 10.1016/j.ins.2023.119470_b0090 article-title: Clustering by detecting density peaks and assigning points by similarity-first search based on weighted K-nearest neighbors graph publication-title: Complexity doi: 10.1155/2020/1731075 – volume: 624 start-page: 252 year: 2023 ident: 10.1016/j.ins.2023.119470_b0100 article-title: An improved density peaks clustering algorithm based on natural neighbor with a merging strategy publication-title: Inf. Sci. doi: 10.1016/j.ins.2022.12.078 – volume: 30 start-page: 985 issue: 4 year: 2019 ident: 10.1016/j.ins.2023.119470_b0190 article-title: A novel cluster validity index based on local cores publication-title: IEEE Trans. Neural Networks Learn. Syst. doi: 10.1109/TNNLS.2018.2853710 – volume: 99 start-page: 135 year: 2016 ident: 10.1016/j.ins.2023.119470_b0115 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: 363 start-page: 149 year: 2019 ident: 10.1016/j.ins.2023.119470_b0035 article-title: Fast and stable clustering analysis based on Grid-mapping K-means algorithm and new clustering validity index publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.07.048 – ident: 10.1016/j.ins.2023.119470_b0050 – ident: 10.1016/j.ins.2023.119470_b0010 doi: 10.1109/ICCSP48568.2020.9182211 – volume: 33 start-page: 2310 issue: 6 year: 2021 ident: 10.1016/j.ins.2023.119470_b0105 article-title: A domain adaptive density clustering algorithm for data with varying density distribution publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2019.2954133 – volume: 34 start-page: 3714 issue: 8 year: 2022 ident: 10.1016/j.ins.2023.119470_b0170 article-title: Distributed density peaks clustering revisited publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2020.3034611 – volume: 7 start-page: 89427 year: 2019 ident: 10.1016/j.ins.2023.119470_b0140 article-title: Differential privacy-preserving density peaks clustering based on shared near neighbors similarity publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2927308 – volume: 7 start-page: 60684 year: 2019 ident: 10.1016/j.ins.2023.119470_b0185 article-title: Efficient clustering method based on density peaks with symmetric neighborhood relationship publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2912332 – volume: 51 start-page: 3653 issue: 7 year: 2021 ident: 10.1016/j.ins.2023.119470_b0065 article-title: Rule-based modeling with DBSCAN-based information granules publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2019.2902603 – volume: 11 start-page: 2837 year: 2010 ident: 10.1016/j.ins.2023.119470_b0145 article-title: Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance publication-title: J. Mach. Learn. Res. – volume: 39 start-page: 1 issue: 1 year: 1977 ident: 10.1016/j.ins.2023.119470_b0055 article-title: Maximum likelihood from incomplete data via the EM algorithm publication-title: J. R. Stat. Soc. Ser. B doi: 10.1111/j.2517-6161.1977.tb01600.x – volume: 33 start-page: 374 issue: 2 year: 2021 ident: 10.1016/j.ins.2023.119470_b0155 article-title: Clustering with local density peaks-based minimum spanning tree publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2019.2930056 – volume: 554 start-page: 61 year: 2021 ident: 10.1016/j.ins.2023.119470_b0130 article-title: A fast density peaks clustering algorithm with sparse search publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.11.050 – volume: 354 start-page: 19 year: 2016 ident: 10.1016/j.ins.2023.119470_b0095 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 – ident: 10.1016/j.ins.2023.119470_b0045 doi: 10.1109/BigComp.2018.00084 – volume: 9 start-page: 743 issue: 5 year: 2018 ident: 10.1016/j.ins.2023.119470_b0075 article-title: DPCG: an efficient density peaks clustering algorithm based on grid publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-016-0603-2 – volume: 158 start-page: 65 year: 2018 ident: 10.1016/j.ins.2023.119470_b0125 article-title: An improved density peaks clustering algorithm with fast finding cluster centers publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2018.05.034 – volume: 128 start-page: 551 year: 2019 ident: 10.1016/j.ins.2023.119470_b0165 article-title: Fast and general density peaks clustering publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2019.10.019 – year: 2022 ident: 10.1016/j.ins.2023.119470_b0150 article-title: Fast LDP-MST: an efficient density-peak-based clustering method for large-size datasets publication-title: IEEE Trans. Knowl. Data Eng. – volume: 344 start-page: 1492 issue: 6191 year: 2014 ident: 10.1016/j.ins.2023.119470_b0070 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – volume: 66 start-page: 3681 issue: 5 year: 2019 ident: 10.1016/j.ins.2023.119470_b0030 article-title: Scalable semisupervised GMM for big data quality prediction in multimode processes publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2018.2856200 – volume: 27 start-page: 1775 issue: 2 year: 2021 ident: 10.1016/j.ins.2023.119470_b0015 article-title: Integrating prior knowledge in mixed-initiative social network clustering publication-title: IEEE Trans. Vis. Comput. Graph. doi: 10.1109/TVCG.2020.3030347 – ident: 10.1016/j.ins.2023.119470_b0040 doi: 10.1109/IWOBI47054.2019.9114411 – volume: 450 start-page: 200 year: 2018 ident: 10.1016/j.ins.2023.119470_b0085 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: 7 start-page: 34301 year: 2019 ident: 10.1016/j.ins.2023.119470_b0120 article-title: Density peaks clustering based on weighted local density sequence and nearest neighbor assignment publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2904254 – volume: 621 start-page: 627 year: 2023 ident: 10.1016/j.ins.2023.119470_b0175 article-title: VDPC: variational density peaks clustering algorithm publication-title: Inf. Sci. doi: 10.1016/j.ins.2022.11.091 – volume: 29 start-page: 1725 issue: 7 year: 2021 ident: 10.1016/j.ins.2023.119470_b0135 article-title: Fuzzy density peaks clustering publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2020.2985004 – ident: 10.1016/j.ins.2023.119470_b0020 doi: 10.1109/ICECA52323.2021.9675894 – volume: 32 start-page: 13465 issue: 17 year: 2020 ident: 10.1016/j.ins.2023.119470_b0180 article-title: McDPC: multi-center density peak clustering publication-title: Neural Comput. & Applic. doi: 10.1007/s00521-020-04754-5 – ident: 10.1016/j.ins.2023.119470_b0195 doi: 10.1109/ICDM51629.2021.00074 – volume: 76 start-page: 695 issue: 4 year: 2020 ident: 10.1016/j.ins.2023.119470_b0025 article-title: The seeding algorithms for spherical k-means clustering publication-title: J. Glob. Optim. doi: 10.1007/s10898-019-00779-w – volume: 136 year: 2023 ident: 10.1016/j.ins.2023.119470_b0060 article-title: A sampling-based density peaks clustering algorithm for large-scale data publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2022.109238 – volume: 11 start-page: 1179 year: 2020 ident: 10.1016/j.ins.2023.119470_b0110 article-title: Mk-NNG-DPC: density peaks clustering based on improved mutual K-nearest-neighbor graph publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-019-01031-3 |
| SSID | ssj0004766 |
| Score | 2.5651746 |
| Snippet | Density peaks clustering (DPC) has had an impact in many fields, as it can quickly select centers and effectively process complex data. However, it also has... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 119470 |
| SubjectTerms | Density peaks clustering Improved mutual K-nearest-neighbor Kd-tree Sparse matrix Sub-cluster merging |
| Title | Fast density peaks clustering algorithm based on improved mutual K-nearest-neighbor and sub-cluster merging |
| URI | https://dx.doi.org/10.1016/j.ins.2023.119470 |
| Volume | 647 |
| WOSCitedRecordID | wos001062207400001&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 issn: 0020-0255 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0004766 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELbQlgMcEBQQbWnlA-JAFGnzto8VarWlqEKiSLlFftJst9nVJqH9-YxjJ5u2FAESl2w0sp0o37f2jD0PhN5JqqMU3tAnWnI_ljKF_1wY-UxETJNUTIm0xSayszOS5_SLc2Kvu3ICWVWRmxu6-q9QgwzANqGzfwH3MCgI4B5AhyvADtc_Av6Y1Y0njV866NcrxS5rTyxakw-hi0dcfF-uy-biyjPrlzRnBWW3rwD3V20XTHLqVyaxbd3AL5ju3LlZ1i333UCeidns17x57ws_xEF6blkd1PXPpTvZXw56syul8vWi1KrspXlrZHm5aTdbdqIZK69dM7dDAUAHt3YohtCZW56dRk_1jUEznopTm33z3rRudxjmYIuYDOthBBM9jW3BkTvZss3hc2cngWllan-CYbwVZgklE7R1eHKUf9oEzWb2ILt_j_7Iu3P-u_OgXystI0Xk_Dl65iwIfGiRf4EeqWobPR3lldxG-y4aBb_HI1iwm8dfokvDEew4gjuO4A1H8MAR3HEEQ9eeI9hyBN_nCAaO4BFHsOPIK_Tt-Oj848x3VTd8EdKs8SXloNQTrVigVchiqsNY0YTzIOFExpkMhDa6jTCmbqZUppMsIjxUEaiiOlXRazSplpV6g_BUUc5DBguHZLGAgeGbBzDiVIBZzhKxg6b9Zy2ES0lvKqMsit73cF4AEoVBorBI7KAPQ5eVzcfyu8Zxj1XhmG8VxQKI9XC33X_rtoeebNj_Fk2adav20WPxoynr9YGj3088D53b |
| 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=Fast+density+peaks+clustering+algorithm+based+on+improved+mutual+K-nearest-neighbor+and+sub-cluster+merging&rft.jtitle=Information+sciences&rft.au=Li%2C+Chao&rft.au=Ding%2C+Shifei&rft.au=Xu%2C+Xiao&rft.au=Hou%2C+Haiwei&rft.date=2023-11-01&rft.pub=Elsevier+Inc&rft.issn=0020-0255&rft.volume=647&rft_id=info:doi/10.1016%2Fj.ins.2023.119470&rft.externalDocID=S0020025523010551 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0020-0255&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0020-0255&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0020-0255&client=summon |