Efficient clustering of large data sets

Abstract

Clustering is an activity of finding abstractions from data and these abstractions can be used for decision making [1]. In this paper, we select the cluster representatives as prototypes for efficient classification [3]. There are a variety of clustering algorithms reported in the literature. However, clustering algorithms that perform multiple scans of large databases (of size in Tera bytes) residing on the disk demand prohibitive computational times. As a consequence, there is a growing interest in designing clustering algorithms that scan the database only once. Algorithms like BIRCH [2], Leader [5] and Single-pass k-means algorithm [4] belong to this category.