Tuning graded possibilistic clustering by visual stability analysis

Authors:
Stefano Rovetta;Francesco Masulli;Tameem Adel
Affiliations:
Dipartimento di Informatica e Scienze dell'Informazione, Università di Genova, Italy;Dipartimento di Informatica e Scienze dell'Informazione, Università di Genova, Italy and Center for Biotechnology, Temple University, Philadelphia;Computer and Systems Engineering Dept., University of Alexandria, Alexandria, Egypt
Venue:
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Year:
2011

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Abstract

When compared to crisp clustering, fuzzy clustering provides more flexible and powerful data representation. However, most fuzzy methods require setting some parameters, as is the case for our Graded Possibilistic c-Means clustering method, which has two parameters in addition to number of centroids. However, for this model selection task there is no well established criterion available. Building on our own previous work on fuzzy clustering similarity indexes, we introduce a technique to evaluate the stability of clusterings by using the fuzzy Jaccard index, and use this procedure to select the most suitable values of parameters. The experiments indicate that the procedure is effective.