PERFORMANCE COMPARATIVE ANALYSIS OF IMPROVED FUZZY AND NON-FUZZY CLASSIFICATION METHODS

ABSTRACT In data mining technology, data clustering has been considered as the most important exploratory data analysis method used to extract the unknown valuable information from the large volume of data for many real time applications. Most of the clustering techniques proved their efficiency in many fields such as medical sciences, earth sciences, decision making systems etc. One of the main approaches to clustering is partition based clustering. This work reports the results of classification performance of three such widely used algorithms namely K-means (KM) or Hard c-means, Fuzzy Possibilistic c-Means (FPCM) and Possibilistic Fuzzy c-Means (PFCM) clustering algorithms. Two well known data sets from UCI machine learning repository are considered to test the algorithms. The efficiency of clustering output is compared with the results observed from the repository. The experimental results demonstrate that FPCM produces close results to PFCM and K-means algorithm yields more accurate results than the FPCM and PFCM algorithms. Keywords