Recently huge amount of data is present in internet as the network technology and the information technology has been developing rapidly. But there is lack of knowledge which becomes a serious problem. The Data mining in the cloud combines application of conventional data mining under the cloud computing. One of the most popular and widely used algorithms in this cloud data mining is the clustering algorithm. The clustering algorithm maps each and every data to one group which is called clusters and hence forms a clean partition of the specified data. In this paper an innovative artificial intelligence approach using Data mining clustering algorithm is introduced. One of the most popular unsupervised clustering algorithms is FCM algorithm. The FCM algorithm is developed based on the fuzzy entropy function. In this, Probability Based Matching (PBM) index as well as F -measure method is used to validate the clustering results. Because there is requirement in FCM algorithm to define the number of clusters and to define the different cluster values corresponding to different fuzzy partitions. From the results it can be shown that the introduced fuzzy c-mean algorithm with fuzzy entropy can achieve better performance compared with the traditional FCM algorithm and the optimum number of clusters can be determined automatically.