Efficient construction of comprehensible hierarchical clusterings

Clustering is an important data mining task which helps in finding useful patterns to summarize the data. In the KDD context, data mining is often used for description purposes rather than for prediction. However, it turns out difficult to find clustering systems that help to ease the interpretation task to the user in both, statistics and Machine Learning fields. In this paper we present ISAAC, a hierarchical clustering system which employs traditional clustering ideas combined with a feature selection mechanism and heuristics in order to provide comprehensible results. At the same time, it allows to efficiently deal with large datasets by means of a preprocessing step. Results suggest that these aims are achieved and encourage further research.