Distributed Data Clustering : A Comparative Analysis

Abstract

Distributed computing plays an important role in the Data Mining process. Cluster analysis is one of the most common techniques in data mining. Clustering is a task of grouping a set of objects in such a way that objects is in the same group. Data mining is a function that assigns items in a collection to target categories or classes. There are many different techniques and algorithms are available for distributed data clustering. Cluster analysis itself is not one specific algorithm, but the general task to be solved. Many researchers have proposed clustering algorithms, which work efficiently in the distributed mining. This paper compares the performance of distributed clustering algorithms namely, Distributed k-means algorithm and partition algorithm. In this research paper we have to discuss, the comparative analysis of some of these distributed clustering.