Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Exploratory mining via constrained frequent set queries
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Using a knowledge cache for interactive discovery of association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Itemset Trees for Targeted Association Querying
IEEE Transactions on Knowledge and Data Engineering
Online mining of fuzzy multidimensional weighted association rules
Applied Intelligence
Association Mining in Large Databases: A Re-examination of Its Measures
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Tuning database configuration parameters with iTuned
Proceedings of the VLDB Endowment
Variance aware optimization of parameterized queries
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
PARAS: interactive parameter space exploration for association rule mining
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
FIRE: interactive visual support for parameter space-driven rule mining
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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 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.