ABSTRACT
Subspace clustering techniques become popular in identifying local patterns from high dimensional data. In this paper, we present a multiobjective optimization based evolutionary algorithm in order to tackle the subspace clustering problem. Previous state-of-the-art algorithms on subspace clustering optimize implicitly or explicitly a single cluster quality measure. The proposed approach optimizes two cluster quality measures namely PBM-index and XB-index simultaneously. The developed algorithm is applied to seven standard real-life datasets for identifying different subspace clusters. Experimentation reveals that the proposed algorithm is able to take advantages of its evolvable genomic structure and multiobjective based framework and it can be applied to any data set.
- Bernard Desgraupes. 2013. Clustering indices. University of Paris Ouest-Lab ModalX 1 (2013), 34.Google Scholar
- Saku Kukkonen and Kalyanmoy Deb. 2006. Improved pruning of non-dominated solutions based on crowding distance for bi-objective optimization problems. In Evolutionary Computation, 2006. CEC 2006. IEEE Congress on. IEEE, 1179--1186.Google ScholarCross Ref
- M. Lichman. 2013. UCI Machine Learning Repository. (2013). http://archive.ics.uci.edu/mlGoogle Scholar
- Emmanuel Müller, Stephan Günnemann, Ira Assent, and Thomas Seidl. 2009. Evaluating clustering in subspace projections of high dimensional data. Proceedings of the VLDB Endowment 2, 1 (2009), 1270--1281. Google ScholarDigital Library
- Anne Patrikainen and Marina Meila. 2006. Comparing subspace clusterings. IEEE Transactions on knowledge and data engineering 18, 7 (2006), 902--916. Google ScholarDigital Library
- Peignier. S. 2017. Subspace clustering on static datasets and dynamic data streams using bio-inspired algorithms. PhD thesis, INSA, Lyon, France. (2017).Google Scholar
- Sriparna Saha and Sanghamitra Bandyopadhyay. 2013. A generalized automatic clustering algorithm in a multiobjective framework. Applied Soft Computing 13, 1 (2013), 89--108. Google ScholarDigital Library
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