Genie+OWA: Robustifying hierarchical clustering with OWA-based linkages
We investigate the application of the Ordered Weighted Averaging (OWA) data fusion operator in agglomerative hierarchical clustering. The examined setting generalises the well-known single, complete and average linkage schemes. It allows to embody expert knowledge in the cluster merge process and to...
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| Published in: | Information sciences Vol. 520; pp. 324 - 336 |
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01.05.2020
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| Abstract | We investigate the application of the Ordered Weighted Averaging (OWA) data fusion operator in agglomerative hierarchical clustering. The examined setting generalises the well-known single, complete and average linkage schemes. It allows to embody expert knowledge in the cluster merge process and to provide a much wider range of possible linkages. We analyse various families of weighting functions on numerous benchmark data sets in order to assess their influence on the resulting cluster structure. Moreover, we inspect the correction for the inequality of cluster size distribution – similar to the one in the Genie algorithm. Our results demonstrate that by robustifying the procedure with the Genie correction, we can obtain a significant performance boost in terms of clustering quality. This is particularly beneficial in the case of the linkages based on the closest distances between clusters, including the single linkage and its “smoothed” counterparts. To explain this behaviour, we propose a new linkage process called three-stage OWA which yields further improvements. This way we confirm the intuition that hierarchical cluster analysis should rather take into account a few nearest neighbours of each point, instead of trying to adapt to their non-local neighbourhood. |
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| AbstractList | We investigate the application of the Ordered Weighted Averaging (OWA) data fusion operator in agglomerative hierarchical clustering. The examined setting generalises the well-known single, complete and average linkage schemes. It allows to embody expert knowledge in the cluster merge process and to provide a much wider range of possible linkages. We analyse various families of weighting functions on numerous benchmark data sets in order to assess their influence on the resulting cluster structure. Moreover, we inspect the correction for the inequality of cluster size distribution – similar to the one in the Genie algorithm. Our results demonstrate that by robustifying the procedure with the Genie correction, we can obtain a significant performance boost in terms of clustering quality. This is particularly beneficial in the case of the linkages based on the closest distances between clusters, including the single linkage and its “smoothed” counterparts. To explain this behaviour, we propose a new linkage process called three-stage OWA which yields further improvements. This way we confirm the intuition that hierarchical cluster analysis should rather take into account a few nearest neighbours of each point, instead of trying to adapt to their non-local neighbourhood. |
| Author | Gagolewski, Marek Cena, Anna |
| Author_xml | – sequence: 1 givenname: Anna orcidid: 0000-0001-8697-5383 surname: Cena fullname: Cena, Anna email: A.Cena@mini.pw.edu.pl organization: Faculty of Mathematics and Information Science, Warsaw University of Technology ul. Koszykowa 75, Warsaw 00-662, Poland – sequence: 2 givenname: Marek orcidid: 0000-0003-0637-6028 surname: Gagolewski fullname: Gagolewski, Marek organization: Faculty of Mathematics and Information Science, Warsaw University of Technology ul. Koszykowa 75, Warsaw 00-662, Poland |
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| Cites_doi | 10.1007/s10489-018-1238-7 10.1109/21.87068 10.18637/jss.v053.i09 10.4316/AECE.2017.04010 10.1016/j.ins.2016.05.003 10.1016/j.ins.2018.11.032 10.1016/j.ins.2017.08.065 10.1016/j.eswa.2011.04.055 10.1080/18756891.2013.859862 10.1093/comjnl/9.4.373 10.1093/bioinformatics/btx810 10.1016/j.ins.2018.04.008 10.1093/comjnl/26.4.354 10.1016/j.fss.2013.01.007 10.1007/BF01908075 10.1109/34.868688 10.1007/BF00535481 10.1109/TFUZZ.2011.2123899 10.1002/int.20097 10.1007/BF02294699 10.1145/233269.233324 10.1007/s10479-014-1589-3 10.1109/3477.891145 10.1016/j.ins.2014.02.062 10.1016/j.ins.2019.03.024 10.1142/S0218488500000290 10.1016/j.fss.2009.10.021 10.1007/s00357-018-9250-5 |
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| Keywords | Aggregation OWA Data fusion Genie Hierarchical clustering |
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| References | Frà nti, Sieranoja (bib0009) 2018; 48 Mayor, Calvo (bib0024) 1997; vol. 1 Gagolewski, Cena, Bartoszuk (bib0011) 2016 Cena (bib0005) 2018 Yager (bib0035) 1988; 18 Yildirim, Birant (bib0038) 2017; 17 Yager (bib0036) 2000; 30 Małyszko, S. T. WierzchoÅ (bib0023) 2007 Graves, Pedrycz (bib0015) 2010; 161 Majewska, Truskolaski (bib0022) 2018 Murtagh (bib0028) 1983; 26 Cena, Gagolewski (bib0006) 2017 Nasıbov, Kandemır-Cavas (bib0029) 2011; 38 Ultsch (bib0032) 2005 Xu (bib0034) 2005; 20 Calvo, Mayor, Torrens, Suner, Mas, Carbonell (bib0004) 2000; 8 Gagolewski, Bartoszuk, Cena (bib0010) 2016; 363 Gomez, Rojas, Montero, Rodriguez, Beliakov (bib0014) 2014; 7 D. Müllner, Modern hierarchical, agglomerative clustering algorithms, arXiv Lance, Williams (bib0019) 1967 Vu, Georgievska, Szoke, Kuzniar, Rober (bib0033) 2018; 34 Djenouri, Belhadi, Fournier-Viger, Lin (bib0007) 2018; 453 Euán, Ombao, Ortega (bib0008) 2018; 35 MacQueen (bib0021) 1967; vol. 1 [stat.ML] (2011). Jain, Dubes (bib0017) 1988 Milligan (bib0025) 1979; 44 Gil-Garcia, Badia-Contelles, Pons-Porrata (bib0013) 2006; vol. 2 Müllner (bib0027) 2013; 53 Lawrence, Phipps (bib0020) 1985; 2 Zhu, Jiang, Evangelidis, Zhang, Pang, Li (bib0041) 2019; 488 Cai, Zhang, Thung, Dai, Hao (bib0003) 2014; 272 Zhou, Wang, Zhang (bib0040) 2020; 509 Beliakov, James, Li (bib0002) 2011; 19 Yahyaoui, Own (bib0037) 2018; 422 Hastie, Tibshirani, Friedman (bib0016) 2009 Beliakov, James (bib0001) 2013; 226 Jamison, Orey, Pruitt (bib0018) 1965; 4 Şeref, Fan, Borenstein, Chaovalitwongse (bib0030) 2018; 263 Gan, Ma, Wu (bib0012) 2007 Shi, Malik (bib0031) 2000; 22 T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for large databases, in: Proc. ACM SIGMOD International Conference on Management of Data – SIGMOD ’96, pp. 103–114. Zhu (10.1016/j.ins.2020.02.025_bib0041) 2019; 488 Lawrence (10.1016/j.ins.2020.02.025_bib0020) 1985; 2 Hastie (10.1016/j.ins.2020.02.025_bib0016) 2009 Euán (10.1016/j.ins.2020.02.025_bib0008) 2018; 35 Graves (10.1016/j.ins.2020.02.025_bib0015) 2010; 161 Milligan (10.1016/j.ins.2020.02.025_bib0025) 1979; 44 Murtagh (10.1016/j.ins.2020.02.025_bib0028) 1983; 26 Shi (10.1016/j.ins.2020.02.025_bib0031) 2000; 22 Jain (10.1016/j.ins.2020.02.025_bib0017) 1988 Gagolewski (10.1016/j.ins.2020.02.025_bib0010) 2016; 363 Majewska (10.1016/j.ins.2020.02.025_bib0022) 2018 Yager (10.1016/j.ins.2020.02.025_bib0036) 2000; 30 Yahyaoui (10.1016/j.ins.2020.02.025_bib0037) 2018; 422 Beliakov (10.1016/j.ins.2020.02.025_bib0002) 2011; 19 Djenouri (10.1016/j.ins.2020.02.025_bib0007) 2018; 453 Yildirim (10.1016/j.ins.2020.02.025_bib0038) 2017; 17 Lance (10.1016/j.ins.2020.02.025_bib0019) 1967 10.1016/j.ins.2020.02.025_bib0026 Cena (10.1016/j.ins.2020.02.025_sbref0006) 2017 Gomez (10.1016/j.ins.2020.02.025_bib0014) 2014; 7 Müllner (10.1016/j.ins.2020.02.025_bib0027) 2013; 53 Ultsch (10.1016/j.ins.2020.02.025_bib0032) 2005 Gan (10.1016/j.ins.2020.02.025_bib0012) 2007 Frà nti (10.1016/j.ins.2020.02.025_bib0009) 2018; 48 Małyszko (10.1016/j.ins.2020.02.025_bib0023) 2007 Vu (10.1016/j.ins.2020.02.025_bib0033) 2018; 34 Calvo (10.1016/j.ins.2020.02.025_bib0004) 2000; 8 Zhou (10.1016/j.ins.2020.02.025_bib0040) 2020; 509 MacQueen (10.1016/j.ins.2020.02.025_bib0021) 1967; vol. 1 Nasıbov (10.1016/j.ins.2020.02.025_bib0029) 2011; 38 Gil-Garcia (10.1016/j.ins.2020.02.025_bib0013) 2006; vol. 2 Cena (10.1016/j.ins.2020.02.025_sbref0005) 2018 Şeref (10.1016/j.ins.2020.02.025_bib0030) 2018; 263 Mayor (10.1016/j.ins.2020.02.025_bib0024) 1997; vol. 1 Cai (10.1016/j.ins.2020.02.025_bib0003) 2014; 272 Gagolewski (10.1016/j.ins.2020.02.025_bib0011) 2016 Beliakov (10.1016/j.ins.2020.02.025_bib0001) 2013; 226 10.1016/j.ins.2020.02.025_bib0039 Xu (10.1016/j.ins.2020.02.025_bib0034) 2005; 20 Jamison (10.1016/j.ins.2020.02.025_bib0018) 1965; 4 Yager (10.1016/j.ins.2020.02.025_bib0035) 1988; 18 |
| References_xml | – volume: 19 start-page: 562 year: 2011 end-page: 574 ident: bib0002 article-title: Learning Choquet-integral-based metrics for semisupervised clustering publication-title: IEEE Trans. Fuzzy Syst. – year: 2009 ident: bib0016 article-title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction – volume: 453 start-page: 154 year: 2018 end-page: 167 ident: bib0007 article-title: Fast and effective cluster-based information retrieval using frequent closed itemsets publication-title: Inf. Sci. – volume: 38 start-page: 12684 year: 2011 end-page: 12690 ident: bib0029 article-title: OWA-based linkage method in hierarchical clustering: application on phylogenetic trees publication-title: Expert Syst. Appl. – volume: 44 start-page: 343 year: 1979 end-page: 346 ident: bib0025 article-title: Ultrametric hierarchical clustering algorithms publication-title: Psychometrika – volume: 7 start-page: 595 year: 2014 end-page: 604 ident: bib0014 article-title: Consistency and stability in aggregation operators: an application to missing data problems publication-title: Int. J. Comput. Intell. Syst. – volume: 363 start-page: 8 year: 2016 end-page: 23 ident: bib0010 article-title: Genie: a new, fast, and outlier-resistant hierarchical clustering algorithm publication-title: Inf. Sci. – volume: 34 start-page: 1577 year: 2018 end-page: 1579 ident: bib0033 article-title: fMLC: fast multi-level clustering and visualization of large molecular datasets publication-title: Bioinformatics – volume: 35 start-page: 71 year: 2018 end-page: 99 ident: bib0008 article-title: The hierarchical spectral merger algorithm: a new time series clustering procedure publication-title: J. Classif. – reference: D. Müllner, Modern hierarchical, agglomerative clustering algorithms, arXiv: – volume: 17 start-page: 77 year: 2017 end-page: 88 ident: bib0038 article-title: K-linkage: a new agglomerative approach for hierarchical clustering publication-title: Adv. Electr. Comput. Eng. – volume: 30 start-page: 835 year: 2000 end-page: 845 ident: bib0036 article-title: Intelligent control of the hierarchical agglomerative clustering process publication-title: IEEE Trans. Syst. ManCybern. Part B – volume: 53 start-page: 1 year: 2013 end-page: 18 ident: bib0027 article-title: Fastcluster: Fast hierarchical, agglomerative clustering routines for R and Python publication-title: J. Stat. Softw. – volume: vol. 1 start-page: 281 year: 1997 end-page: 285 ident: bib0024 article-title: On extended aggregation functions publication-title: Proc. IFSA 1997 – volume: 488 start-page: 205 year: 2019 end-page: 218 ident: bib0041 article-title: Efficient registration of multi-view point sets by k-means clustering publication-title: Inf. Sci. – volume: 509 start-page: 343 year: 2020 end-page: 355 ident: bib0040 article-title: Objective extraction via fuzzy clustering in evolutionary many-objective optimization publication-title: Inf. Sci. – year: 2018 ident: bib0005 publication-title: Adaptive Hierarchical Clustering Algorithms based on Data Aggregation Methods – volume: 161 start-page: 522 year: 2010 end-page: 543 ident: bib0015 article-title: Kernel-based fuzzy clustering: a comparative experimental study publication-title: Fuzzy Sets Syst. – volume: 26 start-page: 354 year: 1983 end-page: 359 ident: bib0028 article-title: A survey of recent advances in hierarchical clustering algorithms publication-title: Comput. J. – volume: 20 start-page: 843 year: 2005 end-page: 865 ident: bib0034 article-title: An overview of methods for determining OWA weights publication-title: Int. J. Intell. Syst. – volume: 18 start-page: 183 year: 1988 end-page: 190 ident: bib0035 article-title: On ordered weighted averaging aggregation operators in multicriteria decision making publication-title: IEEE Trans. Syst. Man Cybern. – volume: vol. 2 start-page: 569 year: 2006 end-page: 572 ident: bib0013 article-title: A general framework for agglomerative hierarchical clustering algorithms publication-title: 18th International Conference on Pattern Recognition (ICPR’06) – volume: 22 start-page: 888 year: 2000 end-page: 905 ident: bib0031 article-title: Normalized cuts and image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 75 year: 2005 end-page: 82 ident: bib0032 article-title: Clustering with SOM: U*C publication-title: Workshop on Self-Organizing Maps – volume: 272 start-page: 29 year: 2014 end-page: 48 ident: bib0003 article-title: A general framework of hierarchical clustering and its applications publication-title: Inf. Sci. – year: 1988 ident: bib0017 article-title: Algorithms for Clustering Data – volume: 263 start-page: 93 year: 2018 end-page: 118 ident: bib0030 article-title: Information-theoretic feature selection with discrete k-median clustering publication-title: Ann. Oper. Res. – reference: T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for large databases, in: Proc. ACM SIGMOD International Conference on Management of Data – SIGMOD ’96, pp. 103–114. – year: 2007 ident: bib0012 article-title: Data Clustering: Theory, Algorithms, and Applications, Philadelphia, Alexandria – start-page: 373 year: 1967 end-page: 380 ident: bib0019 article-title: A general theory of classification sorting strategies: 1. Hierarchical systems publication-title: Comput. J. – start-page: 1 year: 2018 end-page: 21 ident: bib0022 article-title: Cluster-mapping procedure for tourism regions based on geostatistics and fuzzy clustering: example of Polish districts publication-title: Current Issues in Tourism – start-page: 299 year: 2007 end-page: 304 ident: bib0023 article-title: Standard and genetic k-means clustering techniques in image segmentation publication-title: 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM’07) – reference: [stat.ML] (2011). – volume: 226 start-page: 1 year: 2013 end-page: 18 ident: bib0001 article-title: Stability of weighted penalty-based aggregation functions publication-title: Fuzzy Sets Syst. – year: 2017 ident: bib0006 article-title: OWA-based linkage and the Genie correction for hierarchical clustering publication-title: Proc. FUZZ-IEEE’17 – start-page: 191 year: 2016 end-page: 202 ident: bib0011 article-title: Hierarchical clustering via penalty-based aggregation and the Genie approach publication-title: Modeling Decisions for Artificial Intelligence (Lecture Notes in Artificial Intelligence 9880) – volume: 8 start-page: 417 year: 2000 end-page: 451 ident: bib0004 article-title: Generation of weighting triangles associated with aggregation functions publication-title: Int. J. Uncertain. Fuzziness Knowl. Based Syst. – volume: 2 start-page: 193 year: 1985 end-page: 218 ident: bib0020 article-title: Comparing partitions publication-title: J. Classi. – volume: 4 start-page: 40 year: 1965 end-page: 44 ident: bib0018 article-title: Convergence of weighted averages of independent random variables publication-title: Z. Wahrscheinlichkeitstheorie Verw. Geb. – volume: 48 start-page: 4743 year: 2018 end-page: 4759 ident: bib0009 article-title: K-means properties on six clustering benchmark datasets publication-title: Appl. Intell. – volume: 422 start-page: 558 year: 2018 end-page: 571 ident: bib0037 article-title: Unsupervised clustering of service performance behaviors publication-title: Inf. Sci. – volume: vol. 1 start-page: 281 year: 1967 end-page: 297 ident: bib0021 article-title: Some methods for classification and analysis of multivariate observations publication-title: Proc. Fifth Berkeley Symp. on Math. Statist. and Prob. – year: 1988 ident: 10.1016/j.ins.2020.02.025_bib0017 – volume: 48 start-page: 4743 year: 2018 ident: 10.1016/j.ins.2020.02.025_bib0009 article-title: K-means properties on six clustering benchmark datasets publication-title: Appl. Intell. doi: 10.1007/s10489-018-1238-7 – volume: 18 start-page: 183 year: 1988 ident: 10.1016/j.ins.2020.02.025_bib0035 article-title: On ordered weighted averaging aggregation operators in multicriteria decision making publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/21.87068 – volume: 53 start-page: 1 year: 2013 ident: 10.1016/j.ins.2020.02.025_bib0027 article-title: Fastcluster: Fast hierarchical, agglomerative clustering routines for R and Python publication-title: J. Stat. Softw. doi: 10.18637/jss.v053.i09 – volume: 17 start-page: 77 year: 2017 ident: 10.1016/j.ins.2020.02.025_bib0038 article-title: K-linkage: a new agglomerative approach for hierarchical clustering publication-title: Adv. Electr. Comput. Eng. doi: 10.4316/AECE.2017.04010 – volume: 363 start-page: 8 year: 2016 ident: 10.1016/j.ins.2020.02.025_bib0010 article-title: Genie: a new, fast, and outlier-resistant hierarchical clustering algorithm publication-title: Inf. Sci. doi: 10.1016/j.ins.2016.05.003 – start-page: 75 year: 2005 ident: 10.1016/j.ins.2020.02.025_bib0032 article-title: Clustering with SOM: U*C – year: 2009 ident: 10.1016/j.ins.2020.02.025_bib0016 – volume: 509 start-page: 343 year: 2020 ident: 10.1016/j.ins.2020.02.025_bib0040 article-title: Objective extraction via fuzzy clustering in evolutionary many-objective optimization publication-title: Inf. Sci. doi: 10.1016/j.ins.2018.11.032 – volume: vol. 1 start-page: 281 year: 1967 ident: 10.1016/j.ins.2020.02.025_bib0021 article-title: Some methods for classification and analysis of multivariate observations – ident: 10.1016/j.ins.2020.02.025_bib0026 – volume: 422 start-page: 558 year: 2018 ident: 10.1016/j.ins.2020.02.025_bib0037 article-title: Unsupervised clustering of service performance behaviors publication-title: Inf. Sci. doi: 10.1016/j.ins.2017.08.065 – volume: 38 start-page: 12684 year: 2011 ident: 10.1016/j.ins.2020.02.025_bib0029 article-title: OWA-based linkage method in hierarchical clustering: application on phylogenetic trees publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.04.055 – volume: vol. 2 start-page: 569 year: 2006 ident: 10.1016/j.ins.2020.02.025_bib0013 article-title: A general framework for agglomerative hierarchical clustering algorithms – volume: 7 start-page: 595 year: 2014 ident: 10.1016/j.ins.2020.02.025_bib0014 article-title: Consistency and stability in aggregation operators: an application to missing data problems publication-title: Int. J. Comput. Intell. Syst. doi: 10.1080/18756891.2013.859862 – start-page: 373 year: 1967 ident: 10.1016/j.ins.2020.02.025_bib0019 article-title: A general theory of classification sorting strategies: 1. Hierarchical systems publication-title: Comput. J. doi: 10.1093/comjnl/9.4.373 – volume: 34 start-page: 1577 year: 2018 ident: 10.1016/j.ins.2020.02.025_bib0033 article-title: fMLC: fast multi-level clustering and visualization of large molecular datasets publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx810 – volume: 453 start-page: 154 year: 2018 ident: 10.1016/j.ins.2020.02.025_bib0007 article-title: Fast and effective cluster-based information retrieval using frequent closed itemsets publication-title: Inf. Sci. doi: 10.1016/j.ins.2018.04.008 – volume: vol. 1 start-page: 281 year: 1997 ident: 10.1016/j.ins.2020.02.025_bib0024 article-title: On extended aggregation functions – volume: 26 start-page: 354 year: 1983 ident: 10.1016/j.ins.2020.02.025_bib0028 article-title: A survey of recent advances in hierarchical clustering algorithms publication-title: Comput. J. doi: 10.1093/comjnl/26.4.354 – year: 2018 ident: 10.1016/j.ins.2020.02.025_sbref0005 – volume: 226 start-page: 1 year: 2013 ident: 10.1016/j.ins.2020.02.025_bib0001 article-title: Stability of weighted penalty-based aggregation functions publication-title: Fuzzy Sets Syst. doi: 10.1016/j.fss.2013.01.007 – volume: 2 start-page: 193 year: 1985 ident: 10.1016/j.ins.2020.02.025_bib0020 article-title: Comparing partitions publication-title: J. Classi. doi: 10.1007/BF01908075 – volume: 22 start-page: 888 year: 2000 ident: 10.1016/j.ins.2020.02.025_bib0031 article-title: Normalized cuts and image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.868688 – volume: 4 start-page: 40 year: 1965 ident: 10.1016/j.ins.2020.02.025_bib0018 article-title: Convergence of weighted averages of independent random variables publication-title: Z. Wahrscheinlichkeitstheorie Verw. Geb. doi: 10.1007/BF00535481 – volume: 19 start-page: 562 year: 2011 ident: 10.1016/j.ins.2020.02.025_bib0002 article-title: Learning Choquet-integral-based metrics for semisupervised clustering publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2011.2123899 – volume: 20 start-page: 843 year: 2005 ident: 10.1016/j.ins.2020.02.025_bib0034 article-title: An overview of methods for determining OWA weights publication-title: Int. J. Intell. Syst. doi: 10.1002/int.20097 – start-page: 1 year: 2018 ident: 10.1016/j.ins.2020.02.025_bib0022 article-title: Cluster-mapping procedure for tourism regions based on geostatistics and fuzzy clustering: example of Polish districts publication-title: Current Issues in Tourism – volume: 44 start-page: 343 year: 1979 ident: 10.1016/j.ins.2020.02.025_bib0025 article-title: Ultrametric hierarchical clustering algorithms publication-title: Psychometrika doi: 10.1007/BF02294699 – ident: 10.1016/j.ins.2020.02.025_bib0039 doi: 10.1145/233269.233324 – volume: 263 start-page: 93 year: 2018 ident: 10.1016/j.ins.2020.02.025_bib0030 article-title: Information-theoretic feature selection with discrete k-median clustering publication-title: Ann. Oper. Res. doi: 10.1007/s10479-014-1589-3 – start-page: 299 year: 2007 ident: 10.1016/j.ins.2020.02.025_bib0023 article-title: Standard and genetic k-means clustering techniques in image segmentation – volume: 30 start-page: 835 year: 2000 ident: 10.1016/j.ins.2020.02.025_bib0036 article-title: Intelligent control of the hierarchical agglomerative clustering process publication-title: IEEE Trans. Syst. ManCybern. Part B doi: 10.1109/3477.891145 – volume: 272 start-page: 29 year: 2014 ident: 10.1016/j.ins.2020.02.025_bib0003 article-title: A general framework of hierarchical clustering and its applications publication-title: Inf. Sci. doi: 10.1016/j.ins.2014.02.062 – volume: 488 start-page: 205 year: 2019 ident: 10.1016/j.ins.2020.02.025_bib0041 article-title: Efficient registration of multi-view point sets by k-means clustering publication-title: Inf. Sci. doi: 10.1016/j.ins.2019.03.024 – year: 2017 ident: 10.1016/j.ins.2020.02.025_sbref0006 article-title: OWA-based linkage and the Genie correction for hierarchical clustering – volume: 8 start-page: 417 year: 2000 ident: 10.1016/j.ins.2020.02.025_bib0004 article-title: Generation of weighting triangles associated with aggregation functions publication-title: Int. J. Uncertain. Fuzziness Knowl. Based Syst. doi: 10.1142/S0218488500000290 – volume: 161 start-page: 522 year: 2010 ident: 10.1016/j.ins.2020.02.025_bib0015 article-title: Kernel-based fuzzy clustering: a comparative experimental study publication-title: Fuzzy Sets Syst. doi: 10.1016/j.fss.2009.10.021 – start-page: 191 year: 2016 ident: 10.1016/j.ins.2020.02.025_bib0011 article-title: Hierarchical clustering via penalty-based aggregation and the Genie approach – volume: 35 start-page: 71 year: 2018 ident: 10.1016/j.ins.2020.02.025_bib0008 article-title: The hierarchical spectral merger algorithm: a new time series clustering procedure publication-title: J. Classif. doi: 10.1007/s00357-018-9250-5 – year: 2007 ident: 10.1016/j.ins.2020.02.025_bib0012 |
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| Title | Genie+OWA: Robustifying hierarchical clustering with OWA-based linkages |
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