Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2010.207
Title: What is unequal among the equals? Ranking equivalent rules from gene expression data
Authors: Cai, R.
Tung, A.K.H. 
Zhang, Z. 
Hao, Z.
Keywords: Association rules
gene expression data
incremental mining framework
robust classification
Issue Date: 2011
Source: Cai, R.,Tung, A.K.H.,Zhang, Z.,Hao, Z. (2011). What is unequal among the equals? Ranking equivalent rules from gene expression data. IEEE Transactions on Knowledge and Data Engineering 23 (11) : 1735-1747. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2010.207
Abstract: 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. © 2011 IEEE.
Source Title: IEEE Transactions on Knowledge and Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/39201
ISSN: 10414347
DOI: 10.1109/TKDE.2010.207
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