Institute of Information Theories and Applications FOI ITHEA
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.