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 rule relationship analysis, rule redundancy management and rule visualization supporting novel exploratory queries will be showcased. The audience can interactively explore complex rule relationships in a visual manner, such as comparing negative rules with their positive counterparts, that would otherwise take prohibitive time. Overall, our SPIRE system provides data analysts with rich insights into rules and rule relationships while significantly reducing manual effort and time investment required.

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 space-driven approach, our proposed Framework for Interactive Rule Exploration (FIRE) addresses the usability shortcoming. FIRE features innovative visual displays and effective interactions that enable analysts to conduct rule exploration at the speed of thought. Particularly, the parameter space view (PSpace) displays the distribution of rules produced for diverse parameter settings. This not only facilitates user parameter selection but also empowers analyst's to understand rule relationships in the parameter space context. Our user study with 22 subjects establishes the usability and effectiveness of the proposed features and interactions of FIRE using benchmark datasets. Overall, this research encompasses significant contributions at the intersection of data mining, knowledge management and visual analytics.

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.

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 abstractions that form the coarse granularity ruleset space. Based on new insights on the redundancy relationships among rules, PARAS establishes a surprisingly compact representation of complex redundancy relationships while enabling efficient redundancy resolution at query-time. Besides the classical rule mining requests, the PARAS model supports three novel classes of exploratory queries. Using the proposed PSpace index, these exploratory query classes can all be answered with near real-time responsiveness. Our experimental evaluation using several benchmark datasets demonstrates that PARAS achieves 2 to 5 orders of magnitude improvement over state-of-the-art approaches in online association rule mining.