Randomized self-updating process for clustering large-scale data
This paper introduces the randomized self-updating process (rSUP) algorithm for clustering large-scale data. rSUP is an extension of the self-updating process (SUP) algorithm, which has shown effectiveness in clustering data with characteristics such as noise, varying cluster shapes and sizes, and n...
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| Published in | Statistics and computing Vol. 34; no. 1 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.02.2024
Springer Nature B.V |
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| ISSN | 0960-3174 1573-1375 |
| DOI | 10.1007/s11222-023-10355-8 |
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| Abstract | This paper introduces the randomized self-updating process (rSUP) algorithm for clustering large-scale data. rSUP is an extension of the self-updating process (SUP) algorithm, which has shown effectiveness in clustering data with characteristics such as noise, varying cluster shapes and sizes, and numerous clusters. However, SUP’s reliance on pairwise dissimilarities between data points makes it computationally inefficient for large-scale data. To address this challenge, rSUP performs location updates within randomly generated data subsets at each iteration. The Law of Large Numbers guarantees that the clustering results of rSUP converge to those of the original SUP as the partition size grows. This paper demonstrates the effectiveness and computational efficiency of rSUP in large-scale data clustering through simulations and real datasets. |
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| AbstractList | This paper introduces the randomized self-updating process (rSUP) algorithm for clustering large-scale data. rSUP is an extension of the self-updating process (SUP) algorithm, which has shown effectiveness in clustering data with characteristics such as noise, varying cluster shapes and sizes, and numerous clusters. However, SUP’s reliance on pairwise dissimilarities between data points makes it computationally inefficient for large-scale data. To address this challenge, rSUP performs location updates within randomly generated data subsets at each iteration. The Law of Large Numbers guarantees that the clustering results of rSUP converge to those of the original SUP as the partition size grows. This paper demonstrates the effectiveness and computational efficiency of rSUP in large-scale data clustering through simulations and real datasets. |
| ArticleNumber | 47 |
| Author | Chin, Yen-Shiu Shiu, Shang-Ying Lin, Szu-Han Chen, Ting-Li |
| Author_xml | – sequence: 1 givenname: Shang-Ying surname: Shiu fullname: Shiu, Shang-Ying organization: Department of Statistics, National Taipei University – sequence: 2 givenname: Yen-Shiu surname: Chin fullname: Chin, Yen-Shiu organization: Institute of Statistics, National Tsing Hua University – sequence: 3 givenname: Szu-Han surname: Lin fullname: Lin, Szu-Han organization: Institute of Statistical Science, Academia Sinica – sequence: 4 givenname: Ting-Li surname: Chen fullname: Chen, Ting-Li email: tlchen@stat.sinica.edu.tw organization: Institute of Statistical Science, Academia Sinica |
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| Cites_doi | 10.1016/j.ins.2016.05.003 10.1145/276305.276312 10.1016/j.is.2021.101804 10.1007/s10700-018-9290-7 10.1145/1576246.1531327 10.1080/02664763.2012.706268 10.1145/1963405.1963487 10.1080/00949655.2014.949715 10.1080/17445760.2018.1446210 10.1109/TKDE.2002.1033770 10.1080/00949655.2015.1049605 10.1109/TNN.2007.901277 10.1016/j.ins.2022.11.139 10.1109/5.726791 10.1016/j.engappai.2022.104743 10.1145/1835804.1835882 10.18637/jss.v091.i01 10.1007/s10463-013-0443-8 10.1109/PDCAT.2009.46 10.1016/j.patcog.2022.109238 10.1126/science.1242072 10.1016/j.softx.2021.100722 10.1214/13-AOAS680 |
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Math.2015671157176329786210.1007/s10463-013-0443-8 – reference: Lin, S.H., Chen, T.L., Tu, I.P.: Distributed t\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t$$\end{document}-sne. (manuscript) (2023) – reference: VovanTAn improved fuzzy time series forecasting model using variations of dataFuzzy Optim. Decis. Mak.2019182151173394905110.1007/s10700-018-9290-7 – reference: ChenJHHungWLAn automatic clustering algorithm for probability density functionsJ. Stat. Comput. Simul.2015851530473063336960410.1080/00949655.2014.949715 – reference: RodriguezALaioAClustering by fast search and find of density peaksScience201434461911492149610.1126/science.1242072 – reference: BendechacheMTariAKKechadiMTParallel and distributed clustering framework for big spatial data miningInt. J. Parallel Emergent Distrib. 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Sci.201636382310.1016/j.ins.2016.05.003 – reference: HungWLChang-ChienSJYangMSSelf-updating clustering algorithm for estimating the parameters in mixtures of von Mises distributionsJ. Appl. Stat.2012391022592274296802310.1080/02664763.2012.706268 – reference: March, W.B., Ram, P., Gray, A.G.: Fast Euclidean minimum spanning tree: algorithm, analysis, and applications. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 603–612 (2010) – reference: KaufmanLRousseeuwPJFinding Groups in Data: An Introduction to Cluster Analysis2009LondonJohn Wiley & Sons – reference: Sun, T., Shu, C., Li, F., Yu, H., Ma, L., Fang, Y.: An efficient hierarchical clustering method for large datasets with map-reduce. In: 2009 International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 494–499. IEEE (2009) – reference: SchubertERousseeuwPJFast and eager k-medoids clustering: O (k) runtime improvement of the PAM, CLARA, and CLARANS algorithmsInf. Syst.2021101101,80410.1016/j.is.2021.101804 – reference: DingSLiCXuXDingLZhangJGuoLShiTA sampling-based density peaks clustering algorithm for large-scale dataPattern Recogn.2023136109,23810.1016/j.patcog.2022.109238 – reference: Hahsler, M., Piekenbrock, M.: dbscan: Density-based spatial clustering of applications with noise (DBSCAN) and related algorithms (2022). https://CRAN.R-project.org/package=dbscan. R package version 1.1-10 – reference: NgRTHanJClarans: a method for clustering objects for spatial data miningIEEE Trans. Knowl. Data Eng.20021451003101610.1109/TKDE.2002.1033770 – reference: Ikotun, A.M., Ezugwu, A.E., Abualigah, L., Abuhaija, B., Heming, J.: K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data. Inf. Sci. (2022) – reference: LuHPlataniotisKNVenetsanopoulosANMPCA: Multilinear principal component analysis of tensor objectsIEEE Trans. Neural Netw.2008191183910.1109/TNN.2007.901277 – reference: Dong, W., Moses, C., Li, K.: Efficient k-nearest neighbor graph construction for generic similarity measures. In: Proceedings of the 20th International Conference on World Wide Web, pp. 577–586 (2011) – reference: LeCunYBottouLBengioYHaffnerPGradient-based learning applied to document recognitionProc. IEEE199886112278232410.1109/5.726791 – reference: ChenTLHsiehDNHungHTuIPWuPSWuYMChangWHHuangSYγ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma $$\end{document}-SUP: a clustering algorithm for cryo-electron microscopy images of asymmetric particlesAnn. Appl. Stat.201481259285319199010.1214/13-AOAS680 – reference: GuhaSRastogiRShimKCure: an efficient clustering algorithm for large databasesACM SIGMOD Rec.1998272738410.1145/276305.276312 – reference: Chen, T.L., Shiu, S.Y.: A clustering algorithm by self-updating process. In: JSM Proceedings, Statistical Computing Section, Salt Lake City, Utah, pp. 2034–2038 (2007) – reference: Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J Mach. Learn. Res. 9(11) (2008) – ident: 10355_CR31 – volume: 363 start-page: 8 year: 2016 ident: 10355_CR12 publication-title: Inf. Sci. doi: 10.1016/j.ins.2016.05.003 – volume: 27 start-page: 73 issue: 2 year: 1998 ident: 10355_CR13 publication-title: ACM SIGMOD Rec. doi: 10.1145/276305.276312 – ident: 10355_CR29 – volume: 101 start-page: 101,804 year: 2021 ident: 10355_CR26 publication-title: Inf. Syst. doi: 10.1016/j.is.2021.101804 – volume: 18 start-page: 151 issue: 2 year: 2019 ident: 10355_CR30 publication-title: Fuzzy Optim. Decis. Mak. doi: 10.1007/s10700-018-9290-7 – ident: 10355_CR1 doi: 10.1145/1576246.1531327 – ident: 10355_CR14 – ident: 10355_CR6 – volume-title: Finding Groups in Data: An Introduction to Cluster Analysis year: 2009 ident: 10355_CR18 – ident: 10355_CR2 – volume: 39 start-page: 2259 issue: 10 year: 2012 ident: 10355_CR16 publication-title: J. Appl. Stat. doi: 10.1080/02664763.2012.706268 – ident: 10355_CR9 doi: 10.1145/1963405.1963487 – volume: 85 start-page: 3047 issue: 15 year: 2015 ident: 10355_CR5 publication-title: J. Stat. Comput. Simul. doi: 10.1080/00949655.2014.949715 – volume: 34 start-page: 671 issue: 6 year: 2019 ident: 10355_CR3 publication-title: Int. J. Parallel Emergent Distrib. Syst. doi: 10.1080/17445760.2018.1446210 – volume: 14 start-page: 1003 issue: 5 year: 2002 ident: 10355_CR24 publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2002.1033770 – volume: 86 start-page: 1010 issue: 5 year: 2016 ident: 10355_CR27 publication-title: J. Stat. Comput. 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| SubjectTerms | Algorithms Artificial Intelligence Clustering Computational efficiency Computer Science Data points Effectiveness Iterative methods Original Paper Probability and Statistics in Computer Science Statistical Theory and Methods Statistics and Computing/Statistics Programs |
| Title | Randomized self-updating process for clustering large-scale data |
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