Associative classifier explained

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".

Model

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]

Metrics

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.

Implementations

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

Notable implementations include:

• CMAR^[8]
• CPAR^[9]
• L³^{[10] [11]}
• CAEP
• GARC^[12]
• ADT.^[13]

Notes and References

1. Liu. Bing. Hsu. Wynne. Ma. Yiming. 1998. Integrating Classification and Association Rule Mining. 80––86. 10.1.1.48.8380.
2. Thabtah. Fadi. 2007. A review of associative classification mining. The Knowledge Engineering Review. 22. 1. 37–65. 10.1017/s0269888907001026. 15986963 . 0269-8889.
3. Book: Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications. Liao. T Warren. Triantaphyllou. Evangelos. 34599426. 2008. WORLD SCIENTIFIC. 9789812779854. Series on Computers and Operations Research. 10.1142/6689.
4. Web site: CBA homepage. 2018-10-04.
5. Webb . Geoffrey . A Machine Learning Approach to Student Modelling . Proceedings of the Third Australian Joint Conference on Artificial Intelligence (AI 89) . 1989 . 195–205 .
6. Ali. Kamal. Manganaris. Stefanos. Srikant. Ramakrishnan. 1997-08-14. Partial classification using association rules. AAAI Press. 115–118. KDD'97.
7. Book: Wenmin Li. Jiawei Han. Jian Pei. Proceedings 2001 IEEE International Conference on Data Mining . CMAR: Accurate and efficient classification based on multiple class-association rules . 369–376. IEEE Comput. Soc. 10.1109/icdm.2001.989541. 978-0769511191. 2001. 10.1.1.13.219. 2243455.
8. Web site: CMAR Implementation. cgi.csc.liv.ac.uk. 2018-10-04.
9. Web site: THE LUCS-KDD IMPLEMENTATIONS OF THE FOIL, PRM AND CPAR ALGORITHMS. cgi.csc.liv.ac.uk. 2018-10-04.
10. Baralis. E.. Chiusano. S.. Garza. P.. 2008. A Lazy Approach to Associative Classification. IEEE Transactions on Knowledge and Data Engineering. 20. 2. 156–171. 10.1109/tkde.2007.190677. 14829459. 1041-4347.
11. Web site: L3 implementation. dbdmg.polito.it. 2018-10-08.
12. 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. 10.1016/j.dss.2005.03.005. 0167-9236.
13. Book: Wang. Ke. Zhou. Senqiang. He. Yu. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining . Growing decision trees on support-less association rules . 2000. 265–269 . New York, New York, USA. ACM Press. 10.1145/347090.347147. 978-1581132335. 10.1.1.36.9265. 8296096.