Improved Clustering Approach With Validation Measures

In data mining, clustering is a technique in which the set of items are assigned to a group called clusters. Clustering is the most indispensable part of data mining. Fuzzy C means is a well-known and widely used partitional clustering method. K-means clustering is the basic clustering technique and is most widely used algorithm. It is also known as nearest neighbor searching. It simply clusters the datasets into given number of clusters. Numerous efforts have been made to improve the performance of the K-means clustering algorithm but it suffers from two major shortcomings, right value of clusters (k) are initially unknown and effective selections of initial seed are also difficult. In this paper a new idea is generated which overcomes initial seed problem and also the validation of cluster problem. Keywords- K-means, Initial Seed, Validation, Fuzzy Clustering, Efficiency