Increasing cluster uniqueness in Fuzzy C-Means through affinity measure

Clustering is a widely used technique in data mining application for discovering patterns in large dataset. In this paper the Fuzzy C-Means algorithm is analyzed and found that quality of the resultant cluster is based on the initial seed where it is selected either sequentially or randomly. Fuzzy C-Means uses K-Means clustering approach for the initial operation of clustering and then degree of membership is calculated. Fuzzy C-Means is very similar to the K-Means algorithm and hence in this paper K-Means is outlined and proved how the drawback of K-Means algorithm is rectified through UCAM (Unique Clustering with Affinity Measure) clustering algorithm and then UCAM is refined to give a new view namely Fuzzy-UCAM. Fuzzy C-Means algorithm should be initiated with the number of cluster C and initial seeds. For real time large database it´s difficult to predict the number of cluster and initial seeds accurately. In order to overcome this drawback the current paper focused on developing the Fuzzy-UCAM algorithm for clustering without giving initial seed and number of clusters for Fuzzy C-Means. Unique clustering is obtained with the help of affinity measures.