Abstract: In data mining, efforts have focused on finding methods for efficient and effective cluster analysis in large databases. Active themes of research focus on the scalability of clustering methods, the effectiveness of methods for clustering complex shapes and types of data, high-dimensional clustering techniques, and methods for clustering mixed numerical and categorical data in large databases. One of the most accuracy approach based on dynamic modeling of cluster similarity is called Chameleon. In this paper we present a modified hierarchical clustering algorithm that used the main idea of Chameleon and the effectiveness of suggested approach will be demonstrated by the experimental results.

Keywords: Chameleon, clustering, hypergraph partitioning, coarsening hypergraph.

ACM Classification Keywords F.2.1 Numerical Algorithms and Problems

Link:

THE NEW SOFTWARE PACKAGE FOR DYNAMIC HIERARCHICAL CLUSTERING FOR CIRCLES TYPES OF SHAPES

Tetyana Shatovska, Tetiana Safonova, Iurii Tarasov

http://www.foibg.com/ijita/vol15/ijita15-2-p13.pdf