An Adaptive Self-Reduction Type-2 Fuzzy Clustering Algorithm for Pattern Recognition

Abstract

Decisions in real-life can be adversely affected by various uncertainty-sources such as perception-diversity, data-structure and analytical tools. Fuzzy clustering can successfully handle the uncertainties while recognizing patterns in any given data. Nevertheless, type-1 fuzzy clustering techniques has uncertainties on account of precise-nature of primary memberships. Type-2 fuzzy clustering are preferred by many researchers to manage uncertainty in its type-1 version. In type-2 fuzzy clustering, order of fuzziness (fuzzifier) is obtained by interval-valued or general type-2 fuzzy sets. Interval type-2 fuzzy clustering can reduce the computational complexity of type-2 fuzzy set mathematics. However, general type-2 fuzzy clustering scrutinizes uncertainty in fuzzifier using linguistic sets. Interval and general type-2 fuzzy clustering algorithms include type-reduction approaches to obtain type-1 fuzzy sets. Besides, full type-2 fuzzy c-means can be used as a foundation approach in type-2 fuzzy inferences. Although this algorithm includes precise-fuzzifier, it gives a point of view to practically calculate secondary memberships. In this paper, an adaptive type-2 fuzzy clustering algorithm is proposed to manage the uncertainty-sources with a self-reduction procedure. Several numerical results and comparisons are given to demonstrate the achievement of this proposed algorithm. The performance of the proposed algorithm is compared with type-1 and type-2 versions for various multi-dimensional pattern sets from UCI-patterns, Berkeley segmentation database and a real-life application related to sustainable supplier selection in an automotive industry. Consequently, the proposed algorithm reveals fast, convenient and precise results.