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