PARAS: interactive parameter space exploration for association rule mining

Authors:
Abhishek Mukherji;Xika Lin;Christopher R. Botaish;Jason Whitehouse;Elke A. Rundensteiner;Matthew O. Ward;Carolina Ruiz
Affiliations:
Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA;Worcester Polytechnic Institute, Worcester, MA, USA
Venue:
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Year:
2013

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Abstract

We demonstrate our PARAS technology for supporting interactive association mining at near real-time speeds. Key technical innovations of PARAS, in particular, stable region abstractions and rule redundancy management supporting novel parameter space-centric exploratory queries will be showcased. The audience will be able to interactively explore the parameter space view of rules. They will experience near real-time speeds achieved by PARAS for operations, such as comparing rule sets mined using different parameter values, that would otherwise take hours of computation and much manual investigation. Overall, we will demonstrate that the PARAS system provides a rich experience to data analysts through parameter tuning recommendations while significantly reducing the trial-and-error interactions.