Cluster analysis is a descriptive task that seek to identify homogenous group of object and it is also one of the main analytical method in data mining. K-mean is the most popular partitional clustering method. In this paper we discuss standard k-mean algorithm and analyze the shortcoming of k- mean algorithm. In this paper three dissimilar modified k-mean algorithm are discussed which remove the limitation of k-mean algorithm and improve the speed and efficiency of k-mean algorithm. First algorithm remove the requirement of specifying the value of k in advance practically which is very difficult. This algorithm result in optimal number of cluster Second algorithm reduce computational complexity and remove dead unit problem. It select the most populated area as cluster center. Third algorithm use simple data structure that can be used to store information in each iteration and that information can be used in next iteration. It increase the speed of clustering and reduce time complexity.
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