Data clustering has been considered as the most important raw data analysis method used in data mining technology. To extract the unknown valuable information from the large volume of data for so many real time applications are used in data classification. 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. This work reports the results of classification performance of three such widely used algorithms namely K-means (KM), Fuzzy c-means and Possibilistic Fuzzy c-Means (PFCM) clustering algorithms. To analyze these algorithms two known data sets from UCI machine learning repository are taken. From the repository the efficiency of clustering output is compared with the classification performance, percentage of correctness and no. of iterations taken to converge objective function. The experimental results prove that PFCM produces poor results compared to FCM and Kmeans algorithm yields more accurate results than the FCM and PFCM algorithms for liver data.