CMAR: accurate and efficient classification based on multiple class-association rules

By Wenmin Li, Jiawei Han, Jian Pei. 2001

1,493 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Algorithms & Complexity, Signal Processing

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “…into a decision set, where each rule is scored separately according to a scoring function (Rivest, 1987; Clark and Niblett, 1989; Liu et al., 1998; Li et al., 2001; Yin and Han, 2003; Sokolova et al., 2003; Marchand and Sokolova, 2005; Vanhoof and Depaire, 2010; Rudin et al., 2013; Clark and…”
Learning Certifiably Optimal Rule Lists (2017, background): “In associative classiﬁcation (Vanhoof and Depaire, 2010; Liu et al., 1998; Li et al., 2001; Yin and Han, 2003), classiﬁers are often formed greedily from the top down as rule lists, or they are formed by taking the simple union of pre-mined rules, whereby any observation that ﬁts into any of the…”
Learning customized and optimized lists of rules with mathematical programming (2018, background): “[38], Yin and Han [74], Friedman and Popescu [23], Meinshausen [45].”
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, ”
Scalable Bayesian Rule Lists (2016, neutral): “…2005; Marchand & Sokolova, 2005; Rudin, Letham, & Madigan, 2013; Goessling & Kang, 2015), associative classiﬁcation (Vanhoof & Depaire, 2010; Liu et al., 1998; Li, Han, & Pei, 2001; Yin & Han, 2003) and its Bayesian counterparts (McCormick, Rudin, & Madigan, 2012) all fall into this category.”
Learning Optimized Or's of And's (2015, methodology): “…and De Raedt, 1994; Lavrac and Dzeroski, 1994), the associative classification algorithms (Ma, Liu, and Hsu, 1998; Han, Pei, and Yin, 2000; Li, Han, and Pei, 2001; Yin and Han, 2003; Chen et al., 2006; Cheng et al., 2007), and rule induction methods (e.g., Cohen, 1995), there has been…”
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, methodology): “Some of these methods, like CBA, CPAR and CMAR [10, 11, 26, 51] still suffer from a huge number of patterns and do not yield interpretable classifiers, yet it is well-known that for many domains, the space of good predictive models is often large enough to include very simple models [20].”
HARMONY: Efficiently Mining the Best Rules for Classification (2005, background): “…P∪{xj} sup P∪{xj} The highest confidence among the k rules with body P ∪{xj} is called the maximum confidence of local item xj , and is defined as the following: (3.1) max ∀i,1≤i≤k supci P∪{xj} sup P∪{xj} To mine the highest confidence covering rules as quickly as possible, a good heuristic…”
A statistical approach to rule learning (2006, background): “, 1998) and subsequent systems include CMAR (Li et al., 2001) and CPAR (Yin and Han, 2003).”
A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, methodology): “4) Classification based on multiple association rules (CMAR) [14]: This method extends an efficient frequent pattern (FP) mining method, i.”
Structure of association rule classifiers: a review (2010, background): “The CMAR algorithm uses a weighted measure as combined measure, while ARC-AC and ARC-BC calculate the sum of the rules’ confidence levels.”
A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, methodology): “Associative classification and rule learning methods (e.g., Ma et al., 1998; Li et al., 2001; Yin and Han, 2003; Chen et al., 2006; Cheng et al., 2007; McCormick et al., 2012; Rudin et al., 2013; Dong et al., 1999; Michalski, 1969; Clark and Niblett, 1989; Frank and Witten, 1998) mine for frequent…”
A new approach to classification based on association rule mining (2006, methodology): “…the classification based on association rules mining, a well-known method, namely the CBA method proposed by Liu et al. [21] and its modifications [20,23], uses an Apriori-type association rule mining approach [1] to generate classification rules, which usually generates all the frequent…”
From Local Patterns to Global Models: The LeGo Approach to Data Mining (2008, background): “In this section, we give a more detailed description of the main phases in our framework and put them into the context of the state-of-the-art in data mining.”
ORC: Ordered Rules for ClassificationA Discrete Optimization Approach to Associative Classification (2012, methodology): “Association rules were originally designed for data exploration, and later associative classification developed as a framework to use the rules for clas- sification, with algorithms such as CBA, CMAR, and CPAR [Liu et al., 1998, Li et al., 2001, Yin and Han, 2003, Simon et al., 2011].”
A review of associative classification mining (2007, methodology): “In addition, the highest confidence rule may be ineffective, especially for data sets that have unbalanced distribution of class labels (Li et al., 2001; Liu et al., 2003).”
Directional decision lists (2015, methodology): “We end with an experiment on real data from a manufacturing process and determine indicators for problem cases.”