DBSCAN BASED SEED INITIALIZATION OF K-MEANS ALGORITHM

Abstract

This paper proposes effective approach to overcome the problem of finding initial number of clusters for Supervised Data mining algorithms. We present critical review of various approaches that finds the optimal number of clusters for clustering algorithms. In this paper we have used Dbscan algorithms to obtain initial seeds for basic k-means algorithm. To evaluate the proposed approache we have used iris data set, liver disorder dataset and seed dataset.