iPARAS: Incremental Construction of Parameter Space for Online Association Mining

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Xiao Qin, Ramoza Ahsan, Xika Lin, Elke Rundensteiner, Matthew Ward ;
Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, PMLR 36:149-165, 2014.

Abstract

Association rule mining is known to be computationally intensive, yet real-time decision-making applications are increasingly intolerant to delays. The state-of-the-art PARAS solution, a parameter space framework for online association mining, enables efficient rule mining by compactly indexing the final ruleset and providing efficient query-time redundancy resolution. Unfortunately, as many association mining models, PARAS was designed for static data. Modern transaction databases undergo regular data updates that quickly invalidating existing rules or introducing new rules for the PARAS index. While reloading the PARAS index from scratch is impractical, as even upon minor data changes, a complete rule inference and redundancy resolution steps would have to be performed. We now propose to tackle this open problem by designing an incremental parameter space construction approach, called iPARAS, that utilizes the previous mining result to minimally adjust the ruleset and associated redundancy relationships. iPARAS features two innovative techniques. First, iPARAS provides an end-to-end solution, composed of three algorithms, to efficiently update the final ruleset in the parameter space. Second, iPARAS designs a compact data structure to maintain the complex redundancy relationships. Overall, iPARAS achieves several times speed-up on parameter space construction for transaction databases comparing to the state-of-the-art online association rule mining system PARAS.

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