Associative classifier

From HandWiki
Short description: Machine learning model type

An associative classifier (AC) is a kind of supervised learning model that uses association rules to assign a target value. The term associative classification was coined by Bing Liu et al.,[1] in which the authors defined a model made of rules "whose right-hand side are restricted to the classification class attribute".


The model generated by an AC and used to label new records consists of association rules, where the consequent corresponds to the class label. As such, they can also be seen as a list of "if-then" clauses: if the record matches some criteria (expressed in the left side of the rule, also called antecedent), it is then labeled accordingly to the class on the right side of the rule (or consequent).

Most ACs read the list of rules in order, and apply the first matching rule to label the new record.[2]


The rules of an AC inherit some of the metrics of association rules, like the support or the confidence.[3] Metrics can be used to order or filter the rules in the model[4] and to evaluate their quality.


The first proposal of a classification model made of association rules was CBA,[1] although other authors had previously proposed the mining of association rules for classification.[5] Other authors have since then proposed multiple changes to the initial model, like the addition of a redundant rule pruning phase[6] or the exploitation of Emerging Patterns.[7]

Notable implementations include:


  1. 1.0 1.1 Liu, Bing; Hsu, Wynne; Ma, Yiming (1998). Integrating Classification and Association Rule Mining. pp. 80––86. 
  2. Thabtah, Fadi (2007). "A review of associative classification mining". The Knowledge Engineering Review 22 (1): 37–65. doi:10.1017/s0269888907001026. ISSN 0269-8889. 
  3. Liao, T Warren; Triantaphyllou, Evangelos (2008). Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications. Series on Computers and Operations Research. WORLD SCIENTIFIC. doi:10.1142/6689. ISBN 9789812779854. 
  4. "CBA homepage". 
  5. Ali, Kamal; Manganaris, Stefanos; Srikant, Ramakrishnan (1997-08-14). Partial classification using association rules. KDD'97. AAAI Press. pp. 115–118. 
  6. 6.0 6.1 Wenmin Li; Jiawei Han; Jian Pei (2001). CMAR: accurate and efficient classification based on multiple class-association rules. IEEE Comput. Soc. 369–376. doi:10.1109/icdm.2001.989541. ISBN 978-0769511191. 
  7. 7.0 7.1 Dong, Guozhu; Zhang, Xiuzhen; Wong, Limsoon; Li, Jinyan (1999), "CAEP: Classification by Aggregating Emerging Patterns", Discovery Science (Springer Berlin Heidelberg): pp. 30–42, doi:10.1007/3-540-46846-3_4, ISBN 9783540667131, 
  8. "CMAR Implementation". 
  9. Yin, Xiaoxin; Han, Jiawei (2003), "CPAR: Classification based on Predictive Association Rules", Proceedings of the 2003 SIAM International Conference on Data Mining (Society for Industrial and Applied Mathematics): pp. 331–335, doi:10.1137/1.9781611972733.40, ISBN 9780898715453 
  11. Baralis, E.; Chiusano, S.; Garza, P. (2008). "A Lazy Approach to Associative Classification". IEEE Transactions on Knowledge and Data Engineering 20 (2): 156–171. doi:10.1109/tkde.2007.190677. ISSN 1041-4347. 
  12. "L3 implementation". 
  13. Chen, Guoqing; Liu, Hongyan; Yu, Lan; Wei, Qiang; Zhang, Xing (2006). "A new approach to classification based on association rule mining". Decision Support Systems 42 (2): 674–689. doi:10.1016/j.dss.2005.03.005. ISSN 0167-9236. 
  14. Wang, Ke; Zhou, Senqiang; He, Yu (2000). Growing decision trees on support-less association rules. New York, New York, USA: ACM Press. doi:10.1145/347090.347147. ISBN 978-1581132335.