Density Based Clustering Using Gaussian Estimation Technique

Abstract

Density based clustering algorithm (DENCLUE) is one of the primary methods for clustering in data mining. The clusters which are formed based on the density are easy to understand and it does not limit itself to the shapes of clusters. The Denclue algorithm employs a cluster model based on kernel density estimation and a cluster is densed by a local maximum of the estimated density function. Data points are assigned to clusters by hill climbing, i.e. points going to the same local maximum are put into the same cluster. The traditional density estimation is only consider the location of the point, not variable of interest and hill climbing makes unnecessary small steps in the beginning and never converges exactly to the maximum. This paper proposes an improved hill climbing method. The density is measured using influence function and variable interest. The proposed method forms cluster by associating points with density attractors. The clusters are formed well-defined. So the outliers can be detected in efficiently.