Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Heuristic Measures of Interestingness
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
ACM Computing Surveys (CSUR)
GAM: a guidance enabled association mining environment
International Journal of Business Intelligence and Data Mining
Expert Systems with Applications: An International Journal
Summary queries for frequent itemsets mining
Journal of Systems and Software
Is frequency enough for decision makers to make decisions?
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Despite significant progress, determining the interestingness of a rule remains a difficult problem. This short paper investigates the lessons that may be learned from analysing the (largely manual) selection of interesting statistics for cricket (or any other data rich sport) by experts. In particular, the effect of thresholds on the interestingness of rules describing events in the sporting arena is discussed. The concept of anticipation is shown also to be critical in this selection and to vary the level of interest in events that may contribute to the achievement of a threshold value during a match, thus adding a temporal dimension to interestingness. This temporal aspect can be best modelled on the single-past-branching-future model of time. As a result of this investigation, a few new general ideas are discussed that add to the research in this area. Significantly, some of the new criteria are implicitly temporal in that they rely on a model of behaviour over time. The applicability of threshold values for detecting uncharacteristically poor performances are canvassed as areas of interest yet to be explored.