Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A statistical theory for quantitative 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
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Constrained Association Rules to Predict Heart Disease
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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Statistics and data mining: intersecting disciplines
ACM SIGKDD Explorations Newsletter
Current State of Data Mining
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Tight upper bounds on the number of candidate patterns
ACM Transactions on Database Systems (TODS)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
On benchmarking frequent itemset mining algorithms: from measurement to analysis
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Data Mining and Knowledge Discovery
Enhancing Reliability throughout Knowledge Discovery Process
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
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Frequent pattern mining over large database is fundamental to many data mining applications. Various approaches have been proposed for pattern mining with respectable computational performance. However, our study has found some fundamental problems, such as overfitting and probability anomaly, which have not been well addressed. We believe that, analysing and resolving these problems would certainly improve the reliability and usefulness of mining approaches. This paper reports the first part of our study of these fundamental problems, how they are interrelated, and how they impact the correctness and reliability of pattern mining. We also present our proposal to reformulate the measure "support", to resolve the probability anomaly, and to quantify the overfitting degrees, followed by a brief introduction and summary of our proposal to resolve other problems under investigations.