XCFS

Sangeetha Kutty, Richi Nayak, Yuefeng Li
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
An XML clustering algorithm should process both structural and content information of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. This paper introduces a novel approach that first determines structural similarity in the form of frequent subtrees and then uses
more » ... e frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. The proposed method reduces the high dimensionality of input data by using only the structure-constrained content. The empirical analysis reveals that the proposed method can effectively cluster even very large XML datasets and outperform other existing methods.
doi:10.1145/1645953.1646216 dblp:conf/cikm/KuttyNL09 fatcat:abj64ezia5ayhbrfd4mgteqmci