Issue No. 11 - November (2011 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.207
Ruichu Cai , Guandong University of Technology and South China Universty of Technology, Guangzhou
Anthony K.H. Tung , National University of Singapore, Singapore
Zhenjie Zhang , National University of Singapore, Singapore
Zhifeng Hao , Guangdong University of Technology, Guangzhou
In previous studies, association rules have been proven to be useful in classification problems over high dimensional gene expression data. However, due to the nature of such data sets, it is often the case that millions of rules can be derived such that many of them are covered by exactly the same set of training tuples and thus have exactly the same support and confidence. Ranking and selecting useful rules from such equivalent rule groups remain an interesting and unexplored problem. In this paper, we look at two interestingness measures for ranking the interestingness of rules within equivalent rule group: Max-Subrule-Conf and Min-Subrule-Conf. Based on these interestingness measures, an incremental Apriori-like algorithm is designed to select more interesting rules from the lower bound rules of the group. Moreover, we present an improved classification model to fully exploit the potential of the selected rules. Our empirical studies on our proposed methods over five gene expression data sets show that our proposals improve both the efficiency and effectiveness of the rule extraction and classifier construction over gene expression data sets.
Association rules, gene expression data, incremental mining framework, robust classification.
R. Cai, Z. Zhang, Z. Hao and A. K. Tung, "What is Unequal among the Equals? Ranking Equivalent Rules from Gene Expression Data," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 1735-1747, 2010.