Abstract
Periodic-frequent patterns are an important class of regularities that exist in a transactional database. Informally, a frequent pattern is said to be periodic-frequent if it appears at a regular interval specified by the user (i.e., periodically) in a database. A pattern-growth algorithm, called PFP-growth, has been proposed in the literature to discover the patterns. This algorithm constructs a tid-list for a pattern and performs a complete search on the tid-list to determine whether the corresponding pattern is a periodic-frequent or a non-periodic-frequent pattern. In very large databases, the tid-list of a pattern can be very long. As a result, the task of performing a complete search over a pattern’s tid-list can make the pattern mining a computationally expensive process. In this paper, we have made an effort to reduce the computational cost of mining the patterns. In particular, we apply greedy search on a pattern’s tid-list to determine the periodic interestingness of a pattern. The usage of greedy search facilitate us to prune the non-periodic-frequent patterns with a sub-optimal solution, while finds the periodic-frequent patterns with the global optimal solution. Thus, reducing the computational cost of mining the patterns without missing any knowledge pertaining to the periodic-frequent patterns. We introduce two novel pruning techniques, and extend them to improve the performance of PFP-growth. We call the algorithm as PFP-growth++. Experimental results show that PFP-growth++ is runtime efficient and highly scalable as well.
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Kiran, R.U., Kitsuregawa, M. (2014). Novel Techniques to Reduce Search Space in Periodic-Frequent Pattern Mining. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8422. Springer, Cham. https://doi.org/10.1007/978-3-319-05813-9_25
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DOI: https://doi.org/10.1007/978-3-319-05813-9_25
Publisher Name: Springer, Cham
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