Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Mining of Constrained Correlated Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Discovering complex matchings across web query interfaces: a correlation mining approach
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Re-examination of interestingness measures in pattern mining: a unified framework
Data Mining and Knowledge Discovery
Scaling up top-K cosine similarity search
Data & Knowledge Engineering
An XML format for association rule models based on the GUHA method
RuleML'10 Proceedings of the 2010 international conference on Semantic web rules
RP-Tree: rare pattern tree mining
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Cosine interesting pattern discovery
Information Sciences: an International Journal
Hierarchical web-page clustering via in-page and cross-page link structures
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
PARAS: a parameter space framework for online association mining
Proceedings of the VLDB Endowment
Mining frequent correlated graphs with a new measure
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Scaling up cosine interesting pattern discovery: A depth-first method
Information Sciences: an International Journal
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In the literature of data mining and statistics, numerous interestingness measures have been proposed to disclose succinct object relationships of association patterns. However, it is still not clear when a measure is truly effective in large data sets. Recent studies have identified a critical property, null-(transaction)invariance, for measuring event associations in large data sets, but many existing measures do not have this property. We thus re-examine the null-invariant measures and find interestingly that they can be expressed as a generalized mathematical mean, and there exists a total ordering of them. This ordering provides insights into the underlying philosophy of the measures and helps us understand and select the proper measure for different applications.