FRECLE Mining: Discovering Frequent Semantic Tree Cluster Sequences from Historical Tree Structured Data

Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semistructured data, and so on. Existing techniques focus on finding “structural” patterns and ignores the “semantics” that may be associated with the subtrees. In this paper we proposal an algorithm to mine a novel pattern called frequent semantic tree cluster sequences (FRECLE), which captures the frequent sequential association between different semantics of tree-structured data. Given a semantic tree sequence database, the algorithm first categorizes each semantic tree to a semantic cluster. Next, FRECLE patterns are discovered from the semantic cluster sequences by adopting an existing frequent sequential pattern mining algorithm. FRECLE patterns are beneficial in applications where the knowledge of semantic association is significant, such as XML query caching, prefetching XML data, and web users clustering. Specifically, we show how our proposed FRECLE mining framework can be used for designing optimal XML query cache replacement strategy. Finally, by reporting the performance of our algorithm and caching strategy through extensive experiments with both synthetic and real datasets, we show the effectiveness and usefulness of FRECLE mining.