HARMONY: Efficiently Mining the Best Rules for Classification

180 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Boolean Decision Rules via Column Generation (2018, background): “see also the survey [30]) of learning rules one by one and removing “covered” examples, a bottomup strategy of combining more specific rules into more general ones [45, 22, 41], and associative classification in which association rule mining is followed by rule selection using various criteria [38, ”
CBC: An associative classifier with a small number of rules (2014, background): “competitive accuracy performance of associative classifiers, such as CMAR [16], CPAR [28], HARMONY [24], LAC [23] and GARC [5].”
Discriminative Frequent Pattern Analysis for Effective Classification (2007, methodology): “HARMONY [19] is another rule-based classifier which directly mines classification rules.”
Frequent pattern mining: current status and future directions (2007, methodology): “Frequent itemsets have been demonstrated to be useful for classiﬁcation, where association rules are generated and analyzed for use in classiﬁcation (Liu et al. 1998; Dong and Li 1999; Li et al. 2000; Li et al. 2001; Yin and Han 2003; Cong et al. 2005;Wang and Karypis 2005).”
Krimp: mining itemsets that compress (2011, methodology): “However, we are more interested in the comparison to association-rule-based algorithms like iCAEP (Zhang et al. 2000), HARMONY (Wang and Karypis 2005), CBA (Liu et al. 1998) and LB (Meretakis et al. 2000) as these also employ a collection of itemsets for classification.”