A Progressive Clustering Algorithm to Group the XML Data by Structural and Semantic Similarity

Since the emergence in the popularity of XML for data representation and exchange over the Web, the distribution of XML documents has rapidly increased. Therefore it is a new challenge for the field of data mining to turn these documents into a more useful information utility. We present a novel clustering algorithm PCXSS that keeps the heterogeneous XML documents into various groups according to the similar structural and semantic representations. We introduce a global criterion function CPSim that progressively measures the similarity between a XML document and existing clusters, ignoring the need to compute the similarity between two individual documents. The experimental analysis shows the method to be fast and accurate.