CLS and CLS Close: The Scalable Method for Mining the Semi Structured Data Set

Abstract

Semistructured pattern can be formally modeled as Graph Pattern. The most important problem to be solved in mining large semi structured dataset is the scalability of the method. With the successful development of efficient and scalable algorithms for mining frequent itemsets and sequences, it is natural to extend the scope of study to a more general pattern mining problem: mining frequent semistructured patterns or graph patterns. In this paper, we extend the methodology of pattern-growth and develop a novel algorithm called CLS (Canonical Labeling System), which discovers frequent connected subgraphs efficiently using either depth-first search or breadth-first search strategy.