A new heuristic possibilistic clustering algorithm for feature selection
Janusz Kacprzyk, W. Owsinski Jan, A. Viattchenin Dimitri · Journal of Automation Mobile Robotics & Intelligent Systems · 2014
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.