Pruning and summarizing the discovered associations

By B. Liu, Wynne Hsu, Yiming Ma. 1999

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Citations in the corpus

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (2017, methodology): “propose methods for pruning and summarizing learned rules, as well as matching rules by similarity [14, 15].”
Understanding the Crucial Role of Attribute Interaction in Data Mining (2001, methodology): “1 Attribute Interaction as the Key Concept in Pruning and Summarizing Discovered Rules Liu et al. (1999) proposed a method for summarizing and pruning discovered rules.”
Generating non-redundant association rules (2000, background): “Other work addresses the problem of mining interesti ng association rules [8, 3, 10, 12].”
Introduction to arules — Mining Association Rules and Frequent Item Sets (2006, methodology): “…(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 (Hahsler, Hornik, and Reutterer, 2005b) Leverage…”
Mining Optimal Class Association Rule Set (2001, unknown): “[13–15].”
Optimal Action Extraction for Random Forests and Boosted Trees (2015, methodology): “Liu et al. tried to discover actionable knowledge by pruning and summarizing the learnt rules, as well as matching rules by similarity [21, 22].”
Directional decision lists (2015, methodology): “We end with an experiment on real data from a manufacturing process and determine indicators for problem cases.”
Flexible Frameworks for Actionable Knowledge Discovery (2010, methodology): “Additionally, rather than addressing the whole KDD process as it did in some of related works, this paper only focuses on method/algorithm frameworks toward AKD.”
Association mining (2006, methodology): “Other techniques postanalysis techniques of signi.cance have also been proposed [Srikant and Agrawal 1996; Liu et al. 1999].”
Detecting Group Differences: Mining Contrast Sets (2001, methodology): “For example, association rule mining programs can produce thousands of results Liu et al., 1999a which are far too many for a user to view.”