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Association rule mining is known to be computationally intensive, yet real-time decision-making applications are increasingly intolerant to delays. In this paper, we introduce the parameter space model, called PARAS. PARAS enables efficient rule mining by compactly maintaining the final rulesets. The PARAS model is based on the notion of stable region(More)
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(More)
While significant strides have been made on efficient association rule mining, the usability of mining systems woefully lags behind. In particular, the usability of rule mining systems is limited by the lack of support for interactive exploration of the relationships among rule results produced with various parameter settings. Based on a novel parameter(More)
We demonstrate our SPIRE technology for supporting interactive mining of both positive and negative rules at the speed of thought. It is often misleading to learn only about positive rules, yet extremely revealing to find strongly supported negative rules. Key technical contributions of SPIRE including region-wise abstractions of rules, positive-negative(More)
Association rule mining is known to be computationally intensive, yet real-time decisionmaking applications are increasingly intolerant to delays. The state-of-the-art PARAS 1 solution, a parameter space framework for online association mining, enables efficient rule mining by compactly indexing the final ruleset and providing efficient query-time(More)
Traditional temporal association mining systems, once supplied with a specific parameter setting such as time periods of interest, minimum support and confidence, generate the rule set from scratch. This one-at-a-time paradigm forces the analysts to perform successive trial-and-error iterations to finally discover interesting temporal patterns. This process(More)
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