Abstract
Constructing associative classifier is to use the technique of mining association rules to extract attribute-value pairs that are associated with class labels. Since too many such kinds of associations may be generated however, the existing algorithms to finding associations are usually ineffective. It is well known that rough sets theory can be used to select reducts of attributes that represent the original data set. In this paper we present an approach of combining the rough set theory, the association rules mining technique, and the covering method to construct classification rules. With a given decision table, the rough set theory is first used to find all reducts of condition attributes of the decision table. Then an adapted Apriori algorithm to mining association rules is used to find a set of associative classifications from each reduct. And third, all association classification rules are ranked according to their importance, support, and confidence and selected in sequence to build a classifier with high accuracy. An example illustrates how this approach works.
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Han, J., Lin, T.Y., Li, J., Cercone, N. (2007). Constructing Associative Classifiers from Decision Tables. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_36
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DOI: https://doi.org/10.1007/978-3-540-72530-5_36
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