document: Using proportional transportation similarity with learned element semantics for XML document clustering This paper proposes a novel approach to measuring XML document similarity by taking into account the semantics between XML elements . The motivation of the proposed approach is to overcome the problems of `` under-contributionö and `` over-contributionö existing in previous work . The element semantics are learned in an unsupervised way and the Proportional Transportation Similarity is proposed to evaluate XML document similarity by modeling the similarity calculation as a transportation problem . Experiments of clustering are performed on three ACM SIGMOD data sets and results show the favorable performance of the proposed approach .
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