Classification by Association Rule Analysis
Given a training dataset D, build a classifier (or a classification model) from D using an association rule mining algorithm. The model can be used to classify future or test cases.
In the previous section, it was shown that a list of rules can be induced or mined from the data for classification. A decision tree may also be converted to a set of rules. It is thus only natural to expect that association rules  be used for classification as well. Yes, indeed! Since the first classification system (called CBA) that used association rules was reported in , many techniques and systems have been proposed by researchers [2, 3, 4, 6, 7, 8, 13, 15, 16]. CBA is based on class association rules (CAR), which are a special type of association rules with only a class label on the right-hand-side of each rule. Thus, syntactically or semantically there is no difference between a rule generated by a class association rule...
- 1.Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases; 1994.p. 487–99.Google Scholar
- 2.Antonie ML, Zaiane O. Text document categorization by term association. In: Proceedings of the 2nd IEEE International Conference on Data Mining; 2002. p. 19–26.Google Scholar
- 4.Cheng H, Yan X, Han J, Hsu C-W. Discriminative frequent pattern analysis for effective classification. In: Proceedings of the 23rd International Conference on Data Engineering; 2007. p. 706–15.Google Scholar
- 6.Jindal N, Liu B. Identifying comparative sentences in text documents. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 2006.p. 244–51.Google Scholar
- 7.Li J, Dong G, Ramamohanarao K. Making use of the most expressive jumping emerging patterns for classification. In: Advances in Knowledge Discovery and Data Mining, 4th Pacific-Asia Conference; 2000. p.~220–32.Google Scholar
- 8.Li W, Han J, Pei J. CMAR: accurate and efficient classification based on multiple class-association rules. In: Proceedings of the 2001 IEEE International Conference on Data Mining; 2001. p. 369–76.Google Scholar
- 10.Liu B, Hsu W, Ma Y. Integrating classification and association rule mining. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining; 1998. p. 80–6.Google Scholar
- 11.Liu B, Hsu W, Ma Y. Mining association rules with multiple minimum supports. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 1999. p.~337–41.Google Scholar
- 12.Liu B, Zhao K, Benkler J, Xiao W. Rule interestingness analysis using OLAP operations. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2006. p.~297–306.Google Scholar
- 13.Meretakis D, Wüthrich B. Extending naïve Bayes classifiers using long itemsets. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 1999. p.~165–74.Google Scholar
- 14.Mobasher B, Dai H, Luo T, Nakagawa N. Effective personalization based on association rule discovery from web usage data. In: Proceedings of the 3rd ACM Workshop on Web Information and Data Management; 2001. p. 9–15.Google Scholar
- 15.Wang K, Zhou S, He Y. Growing decision trees on support-less association rules. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2000. p.~265–9.Google Scholar