International Journal of Engineering Research

Dinesh P. Pitambare ¹, Pravin M. Kamde ¹

Affiliations
1 Sinhgad College of Engineering, Pune-41, Maharashtra, IN

Abstract

Fuzzy-Rule Based Clustering (FRBC) is proposed, for automatically exploring potential clusters in dataset. This uses supervised classification approach to achieve the unsupervised cluster analysis. Fusion of clustering and fuzzy set theory is nothing but fuzzy clustering, which is appropriate to handle problems with imprecise boundaries of clusters. A fuzzy rule-based classification system is a special case of fuzzy modeling, in which the output of system is crisp and discrete. Fuzzy modeling provides high interpretability and allows working with imprecise data. To explore the clusters in the data patterns, FRBC appends some randomly generated auxiliary patterns to the problem space. It then uses the main data as one class and the auxiliary data as another class to enumerate the unsupervised clustering problem as a supervised classification one.

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Dinesh P. Pitambare ¹, Pravin M. Kamde ¹

Affiliations
1 Sinhgad College of Engineering, Pune-41, Maharashtra, IN

Abstract

Generating fuzzy rules from training data is the most vital assignment in design of fuzzy classification system. In this paper, we present an approach to deal with the classification problem where fuzzy logic is used. We intend to show that fuzzy logic introduces new elements in the identification process, to manage imprecise information. A method to generate set of definitive fuzzy rules from initial training data is introduced. A triangular membership function is used for generating fuzzy rules from training data as they are simpler and more human understandable with high interpretability.

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