In this paper, an unsupervised Mercer kernel based fuzzy c-means (MKFCM) clustering algorithm is proposed, in which the implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. A new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results have demonstrated the promising performance of the MKFCM algorithm in different scenarios.