A clustering algorithm based on an estimated distribution model. (9th December 2005)

• Record Type: Journal Article
• Authors: Tan, Ling; Taniar, David; Smith, Kate A.
• Is Part Of: International journal of business intelligence and data mining. Volume 1:Number 2(2005)
• Page Start: 229
• Page End: 245
• Publication Date: 2005-12-09
• Subjects: estimated distribution algorithms -- clustering algorithms -- clustering metric -- maximum entropy -- data mining -- conditional splitting -- conditional merging -- convergence space -- clustering convergence; 006.312
• Languages: English
• ISSNs: 1743-8187

Abstract

This paper applies an estimated distribution model to clustering problems. The proposed clustering method makes use of an inter-intra cluster metric and performs a conditional split-merge operation. With conditional splitting and merging, the proposed clustering method does not require the information of cluster number and an improved cluster vector is subsequently guaranteed. In addition, this paper compares movement conditions between inter-intra cluster metric and intra cluster metric. It proves that, under some conditions, the intersection of convergence space between inter-intra cluster metric and intra cluster metric is not empty, and neither is the other subset in the convergence space. This sheds light on how much a cluster metric can play in clustering convergence.