The major objective of clustering is to discover collection of comparable objects based on similarity metric. On the other hand, a similarity metric is generally specified by the user according to the requirements for obtaining better results.There are several approaches available for clustering objects. In the proposed approach an effective fuzzy clustering technique is used. Fuzzy Possibilistic C-Means (FPCM) is the effective clustering algorithm available to cluster unlabeled data that produces both membership and typicality values during clustering process. In this approach, the efficiency of the Fuzzy Possibilistic C-means clustering approach is enhanced by using the penalized and compensated constraints. Penalized and Compensated terms are embedded with the Modified fuzzy positivistic clustering method’s objective function to construct the Penalized based FPCM (PFPCM). In order to improve the clustering accuracy, third proposed approach uses the Improved Penalized Fuzzy C-Means (IPFCM). The penalty term takes the spatial dependence of the objects into consideration, which is inspired by theNeighborhood Expectation Maximization (NEM) algorithm and is modified according to the criterion of FCM. In this approach, penalized constraint is improved by using NEM algorithm and it is combined with compensated constraints. The proposed Improved Penalized for Fuzzy C-Means (IPFCM) clustering algorithm, uses improved penalized constraints which will help in better calculation of distance between the clusters and increasing the accuracy of clustering.