Data Mining: Heuristics in Fuzzy Pattern Matching

Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy pattern recognition. It has a number of advantages over other pattern recognition methods, including simpler methods of attribute selection or ability to learn in real time environments. These methods build a prototype for each attribute and combine the partial estimations of each prototype by a fusion operator. One of the major problems of this technique is that it is not able to model the dependencies between attributes since fuzzy pattern matching assumes non interactivity between them, and nowadays there is no heuristic in the literature that solves this problem. In this paper we propose a solution to this problem. In order to keep the good properties of fuzzy pattern matching, this heuristic will have the objective of minimizing the dependencies between attributes modeled. To show the accuracy of the proposed solution, we have tested the method on several data sets.