Multiple co-clusterings
conference contribution
posted on 2018-01-01, 00:00 authored by X Wang, G Yu, C Domeniconi, J Wang, Z Yu, Zili ZhangZili Zhang© 2018 IEEE. The goal of multiple clusterings is to discover multiple independent ways of organizing a dataset into clusters. Current approaches to this problem just focus on one-way clustering. In many real-world applications, though, it's meaningful and desirable to explore alternative two-way clustering (or co-clusterings), where both samples and features are clustered. To tackle this challenge and unexplored problem, in this paper we introduce an approach, called Multiple Co-Clusterings (MultiCC), to discover non-redundant alternative co-clusterings. MultiCC makes use of matrix tri-factorization to optimize the sample-wise and feature-wise co-clustering indicator matrices, and introduces two non-redundancy terms to enforce diversity among co-clusterings. We then combine the objective of matrix tri-factorization and two non-redundancy terms into a unified objective function and introduce an iterative solution to optimize the function. Experimental results show that MultiCC outperforms existing multiple clustering methods, and it can find interesting co-clusters which cannot be discovered by current solutions.
History
Pagination
1308-1313Location
SingaporePublisher DOI
Start date
2018-11-17End date
2018-11-20ISSN
1550-4786ISBN-13
9781538691588Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, IEEETitle of proceedings
IEEE ICDM 2018: International Conference on Data MiningEvent
Data Mining. IEEE International Conference (2018 : Singapore)Publisher
IEEEPlace of publication
Piscataway, N.J.Usage metrics
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