Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Elements of information theory
Elements of information theory
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
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
Signature-Based approach for intrusion detection
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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Identifying and expressing data patterns in form of association rules is a commonly used technique in data mining. Typically, association rules discovery is based on two criteria: support and confidence. In this paper we will briefly discuss the insufficiency on these two criteria, and argue the importance of including interestingness/dependency as a criterion for (association) pattern discovery. From the practical computational perspective, we will show how the proposed criterion grounded on interestingness could be used to improve the efficiency of pattern discovery mechanism. Furthermore, we will show a probabilistic inference mechanism that provides an alternative to pattern discovery. Example illustration and preliminary study for evaluating the proposed approach will be presented.