Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Use a Learned Model: A Two-Stage Approach to Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Biologically relevant association rules for classification of microarray data
ACM SIGAPP Applied Computing Review
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In this paper we investigate a method for classifying microarray data using association rules. Associative classifiers, classification systems based on association rules, show good performance level while being easy to read and understand. This feature is especially attractive for biological data where experts can read and validate the association rules. Relevant features are selected using Support Vector Machines with Recursive Feature Elimination. These features are discretized according to their relative expression levels (upregulated, downregulated or no change) and then they are used to build an associative classifier. The proposed combination proves highly accurate for the studied microarray data collection. In addition the classification rules discovered and employed in the classification process prove to be biologically relevant.