Visually Mining Through Cluster Hierarchies

Abstract

Similarity search in database systems is becoming an increasingly important task in modern application domains such as multimedia, molecular biology, medical imaging, computer aided engineering, marketing and purchasing assistance as well as many others. In this paper, we show how visualizing the hierarchical clustering structure of a database of objects can aid the user in his time consuming task to find similar objects. We present related work and explain its shortcomings which led to the development of our new methods. Based on reachability plots, we introduce approaches which automatically extract the significant clusters in a hierarchical cluster representation along with suitable cluster representatives. These techniques can be used as a basis for visual data mining. We implemented our algorithms resulting in an industrial prototype which we used for the experimental evaluation. This evaluation is based on real world test data sets and points out that our new approaches to automatic cluster recognition and extraction of cluster representatives create meaningful and useful results in comparatively short time.