Author(s)

J. L. A. Rosa, N. F. F Ebecken & M. C. A. Costa

Abstract

Towards on an optimized parallel KNN-FUZZY classification approach J. L. A. Rosa, N. F. F. Ebecken & M. C. A. Costa COPPE / Universidade Federal do Rio de Janeiro, Brazil Abstract This paper presents a classification method based on the KNN-Fuzzy classification algorithm, optimized by a Genetic Algorithm in a pc-cluster parallel environment. Analyses are made upon the results obtained in the classification of a large example in order to demonstrate the proposed approach. The performance assessment is also discussed. 1 Introduction Data classification is one of the most used data mining tasks. Anderbergs [l] defines classification as being the process or act to associate a new item or comment to a category. As example, a person can be classified, according some attributes: sex (female or male), nationality (country where it was born), naturalness (state where it was born), instruction degree (illiterate or not), height (low, high). Most of the knowledge discovery techniques are based on mathem

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