Consensus multiobjective differential crisp clustering for categorical data analysis

Authors:
Indrajit Saha;Dariusz Plewczyński;Ujjwal Maulik;Sanghamitra Bandyopadhyay
Affiliations:
Interdisciplinary Centre for Mathematical and Computational Modeling, University of Warsaw, Warsaw, Poland;Interdisciplinary Centre for Mathematical and Computational Modeling, University of Warsaw, Warsaw, Poland;Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India;Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India
Venue:
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Year:
2010

Citing 3
Cited 0

Algorithms for clustering data

Algorithms for clustering data
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this article, an evolutionary crisp clustering technique is described that uses a new consensus multiobjective differential evolution. The algorithm is therefore able to optimize two conflicting cluster validity measures simultaneously and provides resultant Pareto optimal set of nondominated solutions. Thereafter the problem of choosing the best solution from resultant Pareto optimal set is resolved by creation of consensus clusters using voting procedure. The proposed method is used for analyzing the categorical data where no such natural ordering can be found among the elements in categorical domain. Hence no inherent distance measure, like the Euclidean distance, would work to compute the distance between two categorical objects. Index-coded encoding of the cluster medoids (centres) is used for this purpose. The effectiveness of the proposed technique is provided for artificial and real life categorical data sets. Also statistical significance test has been carried out to establish the statistical significance of the clustering results. Matlab version of the software is available at http://bio.icm.edu.pl/~darman/CMODECC.