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 in Data Mining technology. Most of the clustering techniques proved their efficiency in many fields such as decision making systems, medical sciences, earth sciences, etc. partition based clustering is one of the main approaches used in clustering. This work reports the results of classification performance of four such widely used algorithms namely K-means (KM) or Hard c-means, Fuzzy C-means, Fuzzy Possibilistic c-Means (FPCM) and Possibilistic Fuzzy c-Means (PFCM) clustering algorithms. Well known data set 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 FCM, FPCM and PFCM gives the similar percentage of correctness and classification performance.FCM, FPCM and PFCM results are better than K-means. The experimental results prove that fuzzy clustering algorithms are better than non-fuzzy clustering algorithm.

As in the medical field, for one disease there require samples given by diagnosis. The samples will be analyzed by a doctor or a pharmacist. As the no. of patients increases their samples also increases, there require more time to analyze samples for deciding the stage of the disease. To analyze the sample every time requires a skilled person. The samples can be classified by applying them to clustering algorithms. Data clustering has been considered as the most important raw data analysis method used in data mining technology. Most of the clustering techniques proved their efficiency in many applications such as decision making systems, medical sciences, earth sciences etc. Partition based clustering is one of the main approach in clustering. There are various algorithms of data clustering, every algorithm has its own advantages and disadvantages.Production and hosting by ISPACS GmbH. To analyze these algorithms three known data sets from UCI machine learning repository are taken such as thyroid data, liver and wine. The efficiency of clustering output is compared with the classification performance, percentage of correctness. The experimental results show that K-means and FCM give same performance for liver data. And FCM and FPCM are giving same performance for thyroid and wine data. FPCM has more efficient classification performance in all the given data sets.

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