Fuzzy Miner - A Fuzzy System for Solving Pattern Classification Problems

The purpose of this paper is to study the problem of pattern classification as this is presented in the context of data mining. Among the various approaches we focus on the use of Fuzzy Logic for pattern classification, due to its close relation to human thinking. More specifically, this paper presents a heuristic fuzzy method for the classification of numerical data, followed by the design and the implementation of its corresponding tool ( Fuzzy Miner). The initial idea comes from the fact that fuzzy systems are universal approximators of any real continuous function. An approximation method coming from the domain of fuzzy control is appropriately adjusted into pattern classification and an “adaptive“ procedure is proposed and developed for deriving highly accurate linguistic if-then rules. Extensive simulation tests are performed to demonstrate the performance and advantages of Fuzzy Miner, as well as its potential commercial benefits over a real world scenarion.