Survey Paper on Modified Approach for Kmeans Algorithm

Kmeans type clustering aims at partitioning a data set into clusters such that the objects in a cluster are
compact and the objects in different clusters are well separated. However, most kmeans-type clustering
algorithms rely on only intracluster compactness while overlooking intercluster separation. A series of
new clustering algorithms by extending the existing kmeans-type algorithms is proposed by integrating
both intracluster compactness and intercluster separation. First, a set of new objective functions for
clustering is developed. Based on these objective functions, the corresponding updating rules for the
algorithms are then derived analytically. The new algorithm with new objective function to solve the
problem of intracluster compactness and intercluster separation has been proposed. Proposed FCS based
algorithm works simultaneously on both i.e. intracluster compactness and intercluster separation and it
will give a better performance over existing Kmeans.