CLUSTERING ANALYSIS OF HETEROGENEOUS DATA USING HYBRID CLUSTERING ALGORITHMS

In the present digital scenario there is an overwhelming increase in data, with the increase in data it is urgent to develop an effective and efficient approach to handle and cluster these data for further analysis. This paper includes hybridizations of various algorithms like K-Means, Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO) for clustering analysis of Heterogeneous Data. Some of the past and ongoing work based on cluster analysis is also discussed in this paper. This paper also include several algorithms based on hybridization of different clustering algorithms to tackle the heterogeneity of data. Dealing with heterogeneous data is a challenging task. The existing hybrid system is not much efficient to deal with such data in an efficient manner. We will describe the fundamental aspects of clustered data and will analyze each methodology by doing comparative study of all existing algorithms