Revisiting agglomerative clustering
Hierarchical agglomerative methods stand out as particularly effective and popular approaches for clustering data. Yet, these methods have not been systematically compared regarding the important issue of false positives while searching for clusters. A model of clusters involving a higher density nu...
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| Veröffentlicht in: | Physica A Jg. 585; S. 126433 |
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| Sprache: | Englisch |
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01.01.2022
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| Abstract | Hierarchical agglomerative methods stand out as particularly effective and popular approaches for clustering data. Yet, these methods have not been systematically compared regarding the important issue of false positives while searching for clusters. A model of clusters involving a higher density nucleus surrounded by a transition, followed by outliers is adopted as a means to quantify the relevance of the obtained clusters and address the problem of false positives. Six traditional methodologies, namely the single, average, median, complete, centroid and Ward’s linkage criteria are compared with respect to the adopted model. Unimodal and bimodal datasets obeying uniform, gaussian, exponential and power-law distributions are considered for this comparison. The obtained results include the verification that many methods detect two clusters in unimodal data. The single-linkage method was found to be more resilient to false positives. Also, several methods detected clusters not corresponding directly to the nucleus.
•Six classical agglomerative clustering methods are compared regarding false positives.•The single-linkage led to fewer false-positives in unimodal distributions.•The single-linkage yielded clusters corresponding more closely to the nuclei. |
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| AbstractList | Hierarchical agglomerative methods stand out as particularly effective and popular approaches for clustering data. Yet, these methods have not been systematically compared regarding the important issue of false positives while searching for clusters. A model of clusters involving a higher density nucleus surrounded by a transition, followed by outliers is adopted as a means to quantify the relevance of the obtained clusters and address the problem of false positives. Six traditional methodologies, namely the single, average, median, complete, centroid and Ward’s linkage criteria are compared with respect to the adopted model. Unimodal and bimodal datasets obeying uniform, gaussian, exponential and power-law distributions are considered for this comparison. The obtained results include the verification that many methods detect two clusters in unimodal data. The single-linkage method was found to be more resilient to false positives. Also, several methods detected clusters not corresponding directly to the nucleus.
•Six classical agglomerative clustering methods are compared regarding false positives.•The single-linkage led to fewer false-positives in unimodal distributions.•The single-linkage yielded clusters corresponding more closely to the nuclei. |
| ArticleNumber | 126433 |
| Author | Tokuda, Eric K. Comin, Cesar H. Costa, Luciano da F. |
| Author_xml | – sequence: 1 givenname: Eric K. orcidid: 0000-0002-6159-2500 surname: Tokuda fullname: Tokuda, Eric K. email: tokuda.ek@gmail.com organization: Institute of Physics, University of São Paulo, Av. Trabalhador Sao Carlense, 400, SP, Brazil – sequence: 2 givenname: Cesar H. surname: Comin fullname: Comin, Cesar H. organization: Computer Science Department, Federal University of São Carlos, Rod. Washington Luis, km 235, SP, Brazil – sequence: 3 givenname: Luciano da F. surname: Costa fullname: Costa, Luciano da F. organization: Institute of Physics, University of São Paulo, Av. Trabalhador Sao Carlense, 400, SP, Brazil |
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| Keywords | False positive Clustering Hierarchical clustering Agglomerative clustering |
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| References | Davies, Bouldin (b23) 1979 Dubes, Jain (b12) 1980; 19 Glasbey (b10) 1987; 4 Wei, Liang, Guo, Song, Sun (b15) 2019; 341 Kaufman, Rousseeuw (b5) 2009 Cheng, Zhu, Huang, Wu, Yang (b20) 2019; 10 Gower, Ross (b29) 1969; 18 Street, Wolberg, Mangasarian (b32) 1993 Cohen-Addad, Kanade, Mallmann-Trenn, Mathieu (b19) 2019; 66 McQuitty (b25) 1957; 17 Smith, Everhart, Dickson, Knowler, Johannes (b33) 1988 Rand (b22) 1971; 66 Murtagh, Legendre (b9) 2014; 31 Loewenstein, Portugaly, Fromer, Linial (b3) 2008; 24 Sokal, Michener (b27) 1958; 38 Macnaughton-Smith, Williams, Dale, Mockett (b6) 1964; 202 Sørensen (b26) 1948; 5 Gorman, Sejnowski (b35) 1988; 1 Nash, Sellers, Talbot, Cawthorn, Ford (b36) 1994 Plastria (b17) 1986; 19 Müllner (b30) 2011 McInnes, Healy (b39) 2017 Jain, Murty, Flynn (b1) 1999; 31 Massart, Plastria, Kaufman (b16) 1983; 16 Dua, Graff (b31) 2017 Detrano, Janosi, Steinbrunn, Pfisterer, Schmid, Sandhu, Guppy, Lee, Froelicher (b34) 1989; 64 Cohen, Manion, Morrison (b14) 2013 Franke (b2) 2016; 461 Martínez-Pérez (b8) 2016; 33 Reynolds, Richards, de la I., Rayward-Smith (b11) 2006; 5 Gower (b28) 1967 Moro, Cortez, Rita (b37) 2014; 62 Zeitsch (b4) 2019; 524 Ward (b7) 1963; 58 S. Dasgupta, A cost function for similarity-based hierarchical clustering, in: ACM Symposium on Theory of Computing, 2016, pp. 118–127. Jain, Dubes (b38) 1988 Florek, Łukaszewicz, Perkal, Steinhaus, Zubrzycki (b13) 1951 Campello, Moulavi, Sander (b21) 2013 Sneath (b24) 1957; 17 Dua (10.1016/j.physa.2021.126433_b31) 2017 Massart (10.1016/j.physa.2021.126433_b16) 1983; 16 Cohen (10.1016/j.physa.2021.126433_b14) 2013 Cheng (10.1016/j.physa.2021.126433_b20) 2019; 10 Sneath (10.1016/j.physa.2021.126433_b24) 1957; 17 Glasbey (10.1016/j.physa.2021.126433_b10) 1987; 4 Sørensen (10.1016/j.physa.2021.126433_b26) 1948; 5 Gorman (10.1016/j.physa.2021.126433_b35) 1988; 1 Franke (10.1016/j.physa.2021.126433_b2) 2016; 461 Sokal (10.1016/j.physa.2021.126433_b27) 1958; 38 Plastria (10.1016/j.physa.2021.126433_b17) 1986; 19 10.1016/j.physa.2021.126433_b18 Reynolds (10.1016/j.physa.2021.126433_b11) 2006; 5 Davies (10.1016/j.physa.2021.126433_b23) 1979 Detrano (10.1016/j.physa.2021.126433_b34) 1989; 64 McQuitty (10.1016/j.physa.2021.126433_b25) 1957; 17 Street (10.1016/j.physa.2021.126433_b32) 1993 Smith (10.1016/j.physa.2021.126433_b33) 1988 Macnaughton-Smith (10.1016/j.physa.2021.126433_b6) 1964; 202 Moro (10.1016/j.physa.2021.126433_b37) 2014; 62 Gower (10.1016/j.physa.2021.126433_b28) 1967 Müllner (10.1016/j.physa.2021.126433_b30) 2011 Ward (10.1016/j.physa.2021.126433_b7) 1963; 58 Martínez-Pérez (10.1016/j.physa.2021.126433_b8) 2016; 33 Campello (10.1016/j.physa.2021.126433_b21) 2013 Jain (10.1016/j.physa.2021.126433_b1) 1999; 31 Dubes (10.1016/j.physa.2021.126433_b12) 1980; 19 Nash (10.1016/j.physa.2021.126433_b36) 1994 Gower (10.1016/j.physa.2021.126433_b29) 1969; 18 McInnes (10.1016/j.physa.2021.126433_b39) 2017 Kaufman (10.1016/j.physa.2021.126433_b5) 2009 Murtagh (10.1016/j.physa.2021.126433_b9) 2014; 31 Jain (10.1016/j.physa.2021.126433_b38) 1988 Florek (10.1016/j.physa.2021.126433_b13) 1951 Zeitsch (10.1016/j.physa.2021.126433_b4) 2019; 524 Wei (10.1016/j.physa.2021.126433_b15) 2019; 341 Loewenstein (10.1016/j.physa.2021.126433_b3) 2008; 24 Cohen-Addad (10.1016/j.physa.2021.126433_b19) 2019; 66 Rand (10.1016/j.physa.2021.126433_b22) 1971; 66 |
| References_xml | – volume: 17 start-page: 201 year: 1957 end-page: 226 ident: b24 article-title: The application of computers to taxonomy publication-title: Microbiology – volume: 524 start-page: 737 year: 2019 end-page: 775 ident: b4 article-title: A jump model for credit default swaps with hierarchical clustering publication-title: Physica A – volume: 10 start-page: 1591 year: 2019 end-page: 1602 ident: b20 article-title: A hierarchical clustering algorithm based on noise removal publication-title: Int. J. Mach. Learn. Cybern. – volume: 66 start-page: 846 year: 1971 end-page: 850 ident: b22 article-title: Objective criteria for the evaluation of clustering methods publication-title: J. Amer. Statist. Assoc. – volume: 66 start-page: 1 year: 2019 end-page: 42 ident: b19 article-title: Hierarchical clustering: Objective functions and algorithms publication-title: J. ACM – volume: 341 start-page: 118 year: 2019 end-page: 134 ident: b15 article-title: Hierarchical division clustering framework for categorical data publication-title: Neurocomputing – volume: 31 start-page: 264 year: 1999 end-page: 323 ident: b1 article-title: Data clustering: a review publication-title: ACM Comput. Surv. – volume: 19 start-page: 193 year: 1986 end-page: 196 ident: b17 article-title: Two hierarchies associated with each clustering scheme publication-title: Pattern Recognit. – start-page: 261 year: 1988 ident: b33 article-title: Using the ADAP learning algorithm to forecast the onset of diabetes mellitus publication-title: Proceedings of the Annual Symposium on Computer Application in Medical Care – volume: 64 start-page: 304 year: 1989 end-page: 310 ident: b34 article-title: International application of a new probability algorithm for the diagnosis of coronary artery disease publication-title: Am. J. Cardiol. – volume: 62 start-page: 22 year: 2014 end-page: 31 ident: b37 article-title: A data-driven approach to predict the success of bank telemarketing publication-title: Decis. Support Syst. – volume: 18 start-page: 54 year: 1969 end-page: 64 ident: b29 article-title: Minimum spanning trees and single linkage cluster analysis publication-title: J. R. Stat. Soc. Ser. C. Appl. Stat. – year: 2013 ident: b14 article-title: Research Methods in Education – volume: 461 start-page: 384 year: 2016 end-page: 408 ident: b2 article-title: CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks publication-title: Physica A – volume: 1 start-page: 75 year: 1988 end-page: 89 ident: b35 article-title: Analysis of hidden units in a layered network trained to classify sonar targets publication-title: Neural Netw. – volume: 58 start-page: 236 year: 1963 end-page: 244 ident: b7 article-title: Hierarchical grouping to optimize an objective function publication-title: J. Amer. Statist. Assoc. – start-page: 160 year: 2013 end-page: 172 ident: b21 article-title: Density-based clustering based on hierarchical density estimates publication-title: Pacific-Asia Conference on Knowledge Discovery and Data Mining – volume: 17 start-page: 207 year: 1957 end-page: 229 ident: b25 article-title: Elementary linkage analysis for isolating orthogonal and oblique types and typal relevancies publication-title: Educ. Psychol. Meas. – start-page: 623 year: 1967 end-page: 637 ident: b28 article-title: A comparison of some methods of cluster analysis publication-title: Biometrics – start-page: 33 year: 2017 end-page: 42 ident: b39 article-title: Accelerated hierarchical density based clustering publication-title: 2017 IEEE International Conference on Data Mining Workshops – start-page: 224 year: 1979 end-page: 227 ident: b23 article-title: A cluster separation measure publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 202 start-page: 1034 year: 1964 end-page: 1035 ident: b6 article-title: Dissimilarity analysis: a new technique of hierarchical sub-division publication-title: Nature – volume: 16 start-page: 507 year: 1983 end-page: 516 ident: b16 article-title: Non-hierarchical clustering with MASLOC publication-title: Pattern Recognit. – year: 2011 ident: b30 article-title: Modern hierarchical, agglomerative clustering algorithms – year: 1988 ident: b38 article-title: Algorithms for Clustering Data – volume: 24 start-page: i41 year: 2008 end-page: i49 ident: b3 article-title: Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space publication-title: Bioinformatics – volume: 33 start-page: 118 year: 2016 end-page: 140 ident: b8 article-title: On the properties of publication-title: J. Classification – start-page: 282 year: 1951 end-page: 285 ident: b13 article-title: Sur la liaison et la division des points d’un ensemble fini publication-title: Colloquium Mathematicum, Vol. 2 – year: 1994 ident: b36 article-title: The Population Biology of Abalone (Haliotis species) in Tasmania. I. Blacklip Abalone ( – volume: 4 start-page: 103 year: 1987 end-page: 109 ident: b10 article-title: Complete linkage as a multiple stopping rule for single linkage clustering publication-title: J. Classification – year: 2017 ident: b31 article-title: UCI Machine Learning Repository – volume: 31 start-page: 274 year: 2014 end-page: 295 ident: b9 article-title: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? publication-title: J. Classification – volume: 5 start-page: 475 year: 2006 end-page: 504 ident: b11 article-title: Clustering rules: a comparison of partitioning and hierarchical clustering algorithms publication-title: J. Math. Model. Algorithms – volume: 38 start-page: 1409 year: 1958 end-page: 1438 ident: b27 article-title: A statistical method for evaluation systematic relationships publication-title: Univ. Kans. Sci. Bull. – start-page: 861 year: 1993 end-page: 870 ident: b32 article-title: Nuclear feature extraction for breast tumor diagnosis publication-title: Biomedical Image Processing and Biomedical Visualization, Vol. 1905 – reference: S. Dasgupta, A cost function for similarity-based hierarchical clustering, in: ACM Symposium on Theory of Computing, 2016, pp. 118–127. – volume: 19 start-page: 113 year: 1980 end-page: 228 ident: b12 article-title: Clustering methodologies in exploratory data analysis publication-title: Adv. Comput. – year: 2009 ident: b5 article-title: Finding Groups in Data: An Introduction to Cluster Analysis, Vol. 344 – volume: 5 start-page: 1 year: 1948 end-page: 34 ident: b26 article-title: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons publication-title: Biol. Skr. – volume: 10 start-page: 1591 issue: 7 year: 2019 ident: 10.1016/j.physa.2021.126433_b20 article-title: A hierarchical clustering algorithm based on noise removal publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-018-0836-3 – start-page: 33 year: 2017 ident: 10.1016/j.physa.2021.126433_b39 article-title: Accelerated hierarchical density based clustering – start-page: 224 issue: 2 year: 1979 ident: 10.1016/j.physa.2021.126433_b23 article-title: A cluster separation measure publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.1979.4766909 – start-page: 861 year: 1993 ident: 10.1016/j.physa.2021.126433_b32 article-title: Nuclear feature extraction for breast tumor diagnosis – volume: 5 start-page: 1 year: 1948 ident: 10.1016/j.physa.2021.126433_b26 article-title: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons publication-title: Biol. Skr. – year: 2011 ident: 10.1016/j.physa.2021.126433_b30 – volume: 58 start-page: 236 issue: 301 year: 1963 ident: 10.1016/j.physa.2021.126433_b7 article-title: Hierarchical grouping to optimize an objective function publication-title: J. Amer. Statist. Assoc. doi: 10.1080/01621459.1963.10500845 – volume: 24 start-page: i41 issue: 13 year: 2008 ident: 10.1016/j.physa.2021.126433_b3 article-title: Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space publication-title: Bioinformatics doi: 10.1093/bioinformatics/btn174 – volume: 16 start-page: 507 issue: 5 year: 1983 ident: 10.1016/j.physa.2021.126433_b16 article-title: Non-hierarchical clustering with MASLOC publication-title: Pattern Recognit. doi: 10.1016/0031-3203(83)90055-9 – volume: 524 start-page: 737 year: 2019 ident: 10.1016/j.physa.2021.126433_b4 article-title: A jump model for credit default swaps with hierarchical clustering publication-title: Physica A doi: 10.1016/j.physa.2019.04.255 – volume: 17 start-page: 207 issue: 2 year: 1957 ident: 10.1016/j.physa.2021.126433_b25 article-title: Elementary linkage analysis for isolating orthogonal and oblique types and typal relevancies publication-title: Educ. Psychol. Meas. doi: 10.1177/001316445701700204 – volume: 461 start-page: 384 year: 2016 ident: 10.1016/j.physa.2021.126433_b2 article-title: CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks publication-title: Physica A doi: 10.1016/j.physa.2016.05.063 – volume: 66 start-page: 846 issue: 336 year: 1971 ident: 10.1016/j.physa.2021.126433_b22 article-title: Objective criteria for the evaluation of clustering methods publication-title: J. Amer. Statist. Assoc. doi: 10.1080/01621459.1971.10482356 – start-page: 623 year: 1967 ident: 10.1016/j.physa.2021.126433_b28 article-title: A comparison of some methods of cluster analysis publication-title: Biometrics doi: 10.2307/2528417 – year: 2013 ident: 10.1016/j.physa.2021.126433_b14 – volume: 66 start-page: 1 issue: 4 year: 2019 ident: 10.1016/j.physa.2021.126433_b19 article-title: Hierarchical clustering: Objective functions and algorithms publication-title: J. ACM doi: 10.1145/3321386 – volume: 31 start-page: 274 issue: 3 year: 2014 ident: 10.1016/j.physa.2021.126433_b9 article-title: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? publication-title: J. Classification doi: 10.1007/s00357-014-9161-z – volume: 5 start-page: 475 issue: 4 year: 2006 ident: 10.1016/j.physa.2021.126433_b11 article-title: Clustering rules: a comparison of partitioning and hierarchical clustering algorithms publication-title: J. Math. Model. Algorithms doi: 10.1007/s10852-005-9022-1 – volume: 38 start-page: 1409 year: 1958 ident: 10.1016/j.physa.2021.126433_b27 article-title: A statistical method for evaluation systematic relationships publication-title: Univ. Kans. Sci. Bull. – year: 2009 ident: 10.1016/j.physa.2021.126433_b5 – volume: 4 start-page: 103 issue: 1 year: 1987 ident: 10.1016/j.physa.2021.126433_b10 article-title: Complete linkage as a multiple stopping rule for single linkage clustering publication-title: J. Classification doi: 10.1007/BF01890078 – volume: 19 start-page: 113 year: 1980 ident: 10.1016/j.physa.2021.126433_b12 article-title: Clustering methodologies in exploratory data analysis publication-title: Adv. Comput. doi: 10.1016/S0065-2458(08)60034-0 – volume: 19 start-page: 193 issue: 2 year: 1986 ident: 10.1016/j.physa.2021.126433_b17 article-title: Two hierarchies associated with each clustering scheme publication-title: Pattern Recognit. doi: 10.1016/0031-3203(86)90023-3 – year: 1994 ident: 10.1016/j.physa.2021.126433_b36 – volume: 17 start-page: 201 issue: 1 year: 1957 ident: 10.1016/j.physa.2021.126433_b24 article-title: The application of computers to taxonomy publication-title: Microbiology doi: 10.1099/00221287-17-1-201 – volume: 18 start-page: 54 issue: 1 year: 1969 ident: 10.1016/j.physa.2021.126433_b29 article-title: Minimum spanning trees and single linkage cluster analysis publication-title: J. R. Stat. Soc. Ser. C. Appl. Stat. – volume: 31 start-page: 264 issue: 3 year: 1999 ident: 10.1016/j.physa.2021.126433_b1 article-title: Data clustering: a review publication-title: ACM Comput. Surv. doi: 10.1145/331499.331504 – volume: 33 start-page: 118 issue: 1 year: 2016 ident: 10.1016/j.physa.2021.126433_b8 article-title: On the properties of α-unchaining single linkage hierarchical clustering publication-title: J. Classification doi: 10.1007/s00357-016-9198-2 – year: 2017 ident: 10.1016/j.physa.2021.126433_b31 – volume: 202 start-page: 1034 issue: 4936 year: 1964 ident: 10.1016/j.physa.2021.126433_b6 article-title: Dissimilarity analysis: a new technique of hierarchical sub-division publication-title: Nature doi: 10.1038/2021034a0 – volume: 341 start-page: 118 year: 2019 ident: 10.1016/j.physa.2021.126433_b15 article-title: Hierarchical division clustering framework for categorical data publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.02.043 – ident: 10.1016/j.physa.2021.126433_b18 doi: 10.1145/2897518.2897527 – start-page: 261 year: 1988 ident: 10.1016/j.physa.2021.126433_b33 article-title: Using the ADAP learning algorithm to forecast the onset of diabetes mellitus – volume: 1 start-page: 75 issue: 1 year: 1988 ident: 10.1016/j.physa.2021.126433_b35 article-title: Analysis of hidden units in a layered network trained to classify sonar targets publication-title: Neural Netw. doi: 10.1016/0893-6080(88)90023-8 – year: 1988 ident: 10.1016/j.physa.2021.126433_b38 – start-page: 160 year: 2013 ident: 10.1016/j.physa.2021.126433_b21 article-title: Density-based clustering based on hierarchical density estimates – volume: 64 start-page: 304 issue: 5 year: 1989 ident: 10.1016/j.physa.2021.126433_b34 article-title: International application of a new probability algorithm for the diagnosis of coronary artery disease publication-title: Am. J. Cardiol. doi: 10.1016/0002-9149(89)90524-9 – start-page: 282 year: 1951 ident: 10.1016/j.physa.2021.126433_b13 article-title: Sur la liaison et la division des points d’un ensemble fini – volume: 62 start-page: 22 year: 2014 ident: 10.1016/j.physa.2021.126433_b37 article-title: A data-driven approach to predict the success of bank telemarketing publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2014.03.001 |
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