Constraint-based sequential pattern mining: the consideration of recency and compactness
Decision Support Systems
Visual Mining of Association Rules
Visual Data Mining
Mining changing customer segments in dynamic markets
Expert Systems with Applications: An International Journal
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
A new method for ranking changes in customer's behavioral patterns in department stores
Proceedings of the 11th International Conference on Electronic Commerce
Discovering Trends and Relationships among Rules
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
A Condensed Representation of Itemsets for Analyzing Their Evolution over Time
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Mining sequential patterns in the B2B environment
Journal of Information Science
Measuring similarity in feature space of knowledge entailed by two separate rule sets
Knowledge-Based Systems
Temporal evolution and local patterns
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Association rule variation with respect to time
Proceedings of the CUBE International Information Technology Conference
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Rule discovery is one of the central tasks of data mining. Existing research has produced many algorithms for the purpose. These algorithms, however, often generate too manyrules. In the past few years, rule interestingness techniques were proposed to help the user find interesting rules. These techniques typically employ the dataset as a whole to mine rules, and then filter and/or rank the discovered rules in various ways. In this paper, we argue that this is insufficient. These techniques are unable to answer a question that is of criticalimportance to the application of rules, i.e., can the rules be trusted? In practice, the users are always concerned with the question. They want to know whether the rules indeed represent some true and stable (or reliable)underlying relationships in the domain. If a rule is not stable, does it show any systematic pattern such as a trend? Before any rule can be used, these questions must be answered. This paper proposes a technique to use statistical methods to analyze rules from the temporal dimension to answer these questions. Experimental results show that the proposed technique is very effective.