PARAS: A Parameter Space Framework for Online Association Mining
Association rule mining is known to be computationally intensive, yet real-time decision-making applications are increasingly intolerant to delays. In this paper, the authors introduce the parameter space model, called PARAS. PARAS enable 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.