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Many-view clustering: an illustration using multiple dissimilarity measures

Authors:

Adán José-García,

Julia Handl,

Wilfrido Gómez-Flores,

Mario Garza-FabreAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 213 - 214

https://doi.org/10.1145/3319619.3323365

Published: 13 July 2019 Publication History

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Abstract

Multi-view problems generalize standard machine learning problems to situations in which data entities are described from multiple different perspectives, a situation that arises in many applications due to the consideration of multiple data sources or multiple metrics of dissimilarity between entities. Multi-view algorithms for data clustering offer the opportunity to fully consider and integrate this information during the clustering process, but current algorithms are often limited to the use of two views.

Here, we describe the design of an evolutionary algorithm for the problem of multi-view data clustering. The use of a many-objective evolutionary algorithm addresses limitations of previous work, as the resulting method should be capable of scaling to settings with four or more views. We evaluate the performance of our proposed algorithm for a set of traditional benchmark datasets, where multiple views are derived using distinct measures of dissimilarity. Our results demonstrate the ability of our method to effectively deal with a many-view setting, as well as the performance boost obtained from the integration of complementary measures of dissimilarity for both synthetic and real-world datasets.

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References

[1]

Ariel E Bayá and Pablo M Granitto. 2013. How Many Clusters: A Validation Index for Arbitrary-Shaped Clusters. IEEE/ACM Transactions on Computational Biology and Bioinformatics 10, 2 (2013), 401--14.

Digital Library

Google Scholar

[2]

Guoqing Chao, Shiliang Sun, and Jinbo Bi. 2017. A Survey on Multi-View Clustering. arXiv.org, 1--17. https://doi.org/arXiv:1712.06246{cs.LG}

Google Scholar

[3]

Francisco de A.T. de Carvalho, Yves Lechevallier, and Filipe M. de Melo. 2012. Partitioning Hard Clustering Algorithms based on Multiple Dissimilarity Matrices. Pattern Recognition 45, 1 (2012), 447--464.

Digital Library

Google Scholar

[4]

J.Z. Huang, M.K. Ng, Hongqiang Rong, and Ziehen Li. 2005. Automated Variable Weighting in k-means type Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 5 (2005), 657--668.

Digital Library

Google Scholar

[5]

Cong Liu, Qianqian Chen, Yingxia Chen, and Jie Liu. 2019. A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination. Mathematical Problems in Engineering 2019 (2019), 1--21.

Crossref

Google Scholar

[6]

Cong Liu, Jie Liu, Dunlu Peng, and Chunxue Wu. 2018. A General Multiobjective Clustering Approach Based on Multiple Distance Measures. IEEE Access 6 (2018). 41706--41719.

Crossref

Google Scholar

[7]

Kenneth Price, Rainer Storn, and Jouni Lampinen. 2005. Differential Evolution: A Practical Approach to Global Optimization (natural co ed.). Springer-Verlag Berlin.

Digital Library

Google Scholar

[8]

Qingfu Zhang and Hui Li. 2007. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation 11, 6 (2007), 712--731.

Digital Library

Google Scholar

Cited By

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José-García AJacques JSobanski VDhaenens C(2023)Metaheuristic Biclustering Algorithms: From State-of-the-art to Future OpportunitiesACM Computing Surveys10.1145/361759056:3(1-38)Online publication date: 6-Oct-2023
https://dl.acm.org/doi/10.1145/3617590
José-García AHandl J(2022)What’s in a distance? Exploring the interplay between distance measures and internal cluster validity in multi-objective clusteringNatural Computing10.1007/s11047-022-09909-y22:2(259-270)Online publication date: 22-Aug-2022
https://doi.org/10.1007/s11047-022-09909-y
José-García AJacques JSobanski VDhaenens C(2022)Biclustering Algorithms Based on Metaheuristics: A ReviewMetaheuristics for Machine Learning10.1007/978-981-19-3888-7_2(39-71)Online publication date: 13-Aug-2022
https://doi.org/10.1007/978-981-19-3888-7_2
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Published In

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 13 July 2019

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SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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José-García AJacques JSobanski VDhaenens C(2023)Metaheuristic Biclustering Algorithms: From State-of-the-art to Future OpportunitiesACM Computing Surveys10.1145/361759056:3(1-38)Online publication date: 6-Oct-2023
https://dl.acm.org/doi/10.1145/3617590
José-García AHandl J(2022)What’s in a distance? Exploring the interplay between distance measures and internal cluster validity in multi-objective clusteringNatural Computing10.1007/s11047-022-09909-y22:2(259-270)Online publication date: 22-Aug-2022
https://doi.org/10.1007/s11047-022-09909-y
José-García AJacques JSobanski VDhaenens C(2022)Biclustering Algorithms Based on Metaheuristics: A ReviewMetaheuristics for Machine Learning10.1007/978-981-19-3888-7_2(39-71)Online publication date: 13-Aug-2022
https://doi.org/10.1007/978-981-19-3888-7_2
José-García AJacques JFiliot AHandl JLaunay DSobanski VDhaenens C(2022)Multi-view Clustering of Heterogeneous Health Data: Application to Systemic SclerosisParallel Problem Solving from Nature – PPSN XVII10.1007/978-3-031-14721-0_25(352-367)Online publication date: 15-Aug-2022
https://doi.org/10.1007/978-3-031-14721-0_25
José-García AHandl J(2021)On the Interaction Between Distance Functions and Clustering Criteria in Multi-objective ClusteringEvolutionary Multi-Criterion Optimization10.1007/978-3-030-72062-9_40(504-515)Online publication date: 24-Mar-2021
https://doi.org/10.1007/978-3-030-72062-9_40

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Multi-view document clustering via ensemble method

Multi-view clustering via spectral partitioning and local refinement

Structured multi-view k-means clustering

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