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WIREs Data Mining Knowl Discov
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Fuzzy association rule mining framework and its application to effective fuzzy associative classification

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Classification is a technique widely and successfully used for prediction, which is one of the most attractive features of data mining. However, building the classifier is the most challenging part of the process, which proceeds into testing the classifier to check its effectiveness. This article introduces a classification framework that integrates fuzzy association rules into the learning process of machine learning techniques. The integrated framework involves three major components. First, we employ multiobjective optimization twice to decide on the fuzzy sets and then optimize their ranges to extract a set of interesting fuzzy association rules. Second, we use a special subset of the extracted fuzzy association rules, namely, fuzzy class association rules, for building a set of new feature vectors that measure the compatibility between the rules and the given data objects. Third, we train a classifier on the generated feature vectors to predict the class of unseen objects. Most of the earlier algorithms proposed for mining fuzzy association rules assume that the fuzzy sets are given. However, the fuzzy association rule mining component of the proposed framework uses an automated method for autonomous mining of both fuzzy sets and fuzzy association rules. For this purpose, first fuzzy sets are constructed by using a multiobjective genetic algorithm based clustering method for determining and optimizing the membership functions of the fuzzy sets. Then, a method is applied to extract interesting fuzzy association rules. Further, the proposed framework integrates a new layer to the learning process of the machine learning algorithm by constructing the compatibility rule‐based feature vectors; this satisfies the aim of better understandability. Once used by the learning algorithm, the compatibility feature vectors represent a rich source of discrimination knowledge that can substantially impact the prediction power of the final classifier. The experimental study and the reported results show the efficiency and effectiveness of our framework for benchmark datasets. In order to further demonstrate and evaluate the applicability of the proposed method to a variety of domains, it is utilized for the task of gene expression classification as well. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 477–495 DOI: 10.1002/widm.40

Figure 1.

The architecture of FARSVM: components and data flow.

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Figure 2.

Example of generated fuzzy class association rules (FCARs) with their weights.

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Figure 3.

Example of constructing compatibility feature vectors.

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Figure 4.

Number of interesting rules for different values of minimum support.

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Figure 5.

Number of interesting rules obtained when the number of fuzzy sets and discrete intervals is increased to 5.

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Figure 6.

Number of interesting rules for different minimum confidence values.

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Technologies > Association Rules
Technologies > Classification

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