Efficient algorithms for mining frequent and closed patterns from semi-structured data

In this talk, we study effcient algorithms that find frequent patterns and maximal (or closed) patterns from large collections of semi-structured data. We review basic techniques developed by the authors, called the rightmost expansion and the PPC-extension, respectively, for designing efficient frequent and maximal/closed pattern mining algorithms for large semi-structured data. Then, we discuss their applications to design of polynomial-delay and polynomial-space algorithms for frequent and maximal pattern mining of sets, sequences, trees, and graphs.