Clustering XML Documents Using Frequent Edge-Sets

Abstract

Clustering of XML documents is a useful technique for knowledge discovery in XML databases. However, the process of clustering XML documents is always time-consuming due to the semi-structured characteristics of the documents. In this paper, we present an efficient clustering algorithm called Frequent Edge-based XML Clustering (FEXC) to cluster XML documents using frequent edge sets. First, we represent XML documents using edge sets, and then discover the frequent edge sets for each document employing a traditional frequent pattern mining approach. Second, for each frequent edge set, we find all the documents containing it, and then compute a measure called entropy overlap, which indicates the document relevance (overlap) with the ones containing all other frequent edge sets. Clustering is then performed using the entropy overlap measure. Third, we perform a merging process which removes redundant clusters, therefore reducing the number of clusters. Experimental results show that our proposed method outperforms the traditional distance-based XML clustering algorithm in terms of efficiency without compromising the quality of clustering.