Alternative Interest Measures for Mining Associations in Databases

By Edward Omiecinski. IEEE Transactions on Knowledge and Data Engineering, 2003

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Flexible Frameworks for Actionable Knowledge Discovery (2010, background): “…that these frameworks are effective and flexible for extracting actionable knowledge in complex real-world situations, and assist data mining practitioners with catering for their business requirements, needs, and decision-making actions on the findings and deliverables in the business environment.”
Frequent pattern mining: current status and future directions (2007, background): “…mining of frequent patterns; building alternative trees; exploring top-down and bottom-up traversal of such trees in pattern-growth mining by Liu et al. (2002; 2003); and an array-based implementation of preﬁx-tree-structure for efﬁcient pattern growth mining by Grahne and Zhu (2003).”
Association Rules Mining: A Recent Overview (2006, background): “Omiecinski [22] concentrates on finding associations, but with a different slant.”
Introduction to arules — Mining Association Rules and Frequent Item Sets (2006, methodology): “For example, available measures for itemsets are: All-confidence (Omiecinski, 2003) Cross-support ratio (Xiong, Tan, and Kumar, 2003) For rules the following measures are implemented: Chi square measure (Liu, Hsu, and Ma, 1999) Conviction (Brin et al., 1997) Hyper-lift and hyper-confidence…”