Data weighing mechanisms for clustering ensembles

Authors:
Hamid Parvin;Behrouz Minaei-Bidgoli;Hamid Alinejad-Rokny;William F. Punch
Affiliations:
Department of Computer Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran;Department of Computer Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran;Complex Systems in Biology Group, Centre for Vascular Research, Faculty of Medicine, The University of New South Wales, Sydney, NSW, Australia and School of Computer Science and Engineering, The U ...;Department of Computer Science & Engineering, Michigan State University, Engineering Building, East Lansing, USA
Venue:
Computers and Electrical Engineering
Year:
2013

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Abstract

Inspired by bagging and boosting algorithms in classification, the non-weighing and weighing-based sampling approaches for clustering are proposed and studied in the paper. The effectiveness of non-weighing-based sampling technique, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms have been invested in this paper. Experimental results have shown improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and boosting techniques. Subsamples of small size can reduce the computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical research study also compares the performance of boosting and bagging clustering ensembles using different consensus functions on a number of datasets.