A New Heuristic Possibilistic Clustering Algorithm for Feature Selection

The paper deals with the problem of selection of the most informative features. A new effective and efficient heuristic possibilistic clustering algorithm for feature selection is proposed. First, a brief description of basic concepts of the heuristic approach to possibilistic clustering is provided. A technique of initial data pre- processing is described and a fuzzy correlation measure is considered. The new algorithm is described and then illustrated on the well-known Iris data set benchmark and the results obtained are compared with those by us- ing the conventional, well-known and widely employed method of principal component analysis (PCA). Conclu- sions and suggestions for future research are given.

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