Building a Concept Hierarchy from a Distance Matrix

Concept hierarchies are important in many generalized data mining applications, such as multiple level association rule mining. In literature, concept hierarchy is usually given by domain experts. In this paper, we propose algorithms to automatically build a concept hierarchy from a provided distance matrix. Our approach is modifying the traditional hierarchical clustering algorithms. For the purpose of algorithm evaluation, a distance matrix is derived from the concept hierarchy built by our algorithm. Root mean squared error between the provided distant matrix and the derived distance matrix is used as evaluation criterion. We compare the traditional hierarchical clustering and our modified algorithm under three strategies of computing cluster distance, namely single link, average link, and complete link. Empirical results show that the traditional algorithm under complete link strategy performs better than the other strategies. Our modified algorithms perform almost the same under the three strategies; and our algorithms perform better than the traditional algorithms under various situations.