A new approach to classification based on association rule mining

By Guoqing Chen, Hongyan Liu, Lan Yu, Qiang Wei, Xing Zhang. 2006

193 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

How researchers see this paper

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

In Artificial Intelligence

1. Tong Wang
2. Houtao Deng
3. Cynthia Rudin
4. Marco F. Huber
5. Finale Doshi-Velez
6. George C. Runger
7. Erica Klampfl
8. Yimin Liu

In Computational Theory and Mathematics

1. Houtao Deng
2. Tong Wang
3. Finale Doshi-Velez
4. Erica Klampfl
5. Yimin Liu
6. Perry MacNeille
7. Sanjeeb Dash
8. Eugene Tuv

In Information Systems

1. Houtao Deng
2. Cynthia Rudin
3. Tong Wang
4. Finale Doshi-Velez
5. Erica Klampfl
6. Yimin Liu
7. Perry MacNeille
8. Eugene Tuv

Citations in the corpus

• 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, ”
• Learning Optimized Or's of And's (2015, methodology): “…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 little in the way of algorithms designed for…”
• 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].”
• CBC: An associative classifier with a small number of rules (2014, methodology): “competitive accuracy performance of associative classifiers, such as CMAR [16], CPAR [28], HARMONY [24], LAC [23] and GARC [5].”
• A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, methodology): “Then we use information gain to screen rules, similarly to other works (Chen et al., 2006; Cheng et al., 2007).”
• A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner (2010, methodology): “[7] TACO-based rule extraction from ANN 88.”