A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory

This paper discusses the clustering quality and complexities of the hierarchical data clustering algorithm based on gravity theory. The gravitybased clustering algorithm simulates how the given N nodes in a K-dimensional continuous vector space will cluster due to the gravity force, provided that each node is associated with a mass. One of the main issues studied in this paper is how the order of the distance term in the denominator of the gravity force formula impacts clustering quality. The study reveals that, among the hierarchical clustering algorithms invoked for comparison, only the gravitybased algorithm with a high order of the distance term neither has a bias towards spherical clusters nor suffers the well-known chaining effect. Since bias towards spherical clusters and the chaining effect are two major problems with respect to clustering quality, eliminating both implies that high clustering quality is achieved. As far as time complexity and space complexity are concerned, th...

• Type: Conference
• Year: 2001
• Where: PKDD
• Authors: Yen-Jen Oyang, Chien-Yu Chen, Tsui-Wei Yang
• Keywords: Clustering Algorithms, Clustering Quality, Data Clustering Algorithms, Data Mining, PKDD 2001