Deriving quantitative models for correlation clusters
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Two-stage classification methods for microarray data
Expert Systems with Applications: An International Journal
Efficient mining of salinity and temperature association rules from ARGO data
Expert Systems with Applications: An International Journal
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining Association Rule Bases from Integrated Genomic Data and Annotations
Computational Intelligence Methods for Bioinformatics and Biostatistics
Minimum variance associations: discovering relationships in numerical data
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Integrated Computer-Aided Engineering
Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering
International Journal of Data Mining and Bioinformatics
WF-MSB: A weighted fuzzy-based biclustering method for gene expression data
International Journal of Data Mining and Bioinformatics
Frequent pattern discovery without binarization: mining attribute profiles
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
An association rule analysis framework for complex physiological and genetic data
HIS'12 Proceedings of the First international conference on Health Information Science
Effect of data discretization on the classification accuracy in a high-dimensional framework
International Journal of Intelligent Systems
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Motivation: We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we investigate the use of quantitative association rules that can operate directly on numeric data and represent cumulative effects of variables. Technically speaking, this type of quantitative association rules based on half-spaces can find non-axis-parallel regularities. Results: We performed a variety of experiments testing the utility of quantitative association rules for microarray data. First of all, the results should be statistically significant and robust against fluctuations in the data. Next, the approach should be scalable in the number of variables, which is important for such high-dimensional data. Finally, the rules should make sense biologically and be sufficiently different from rules found in regular association rule mining working with discretizations. In all of these dimensions, the proposed approach performed satisfactorily. Therefore, quantitative association rules based on half-spaces should be considered as a tool for the analysis of microarray gene-expression data. Availability: The code is available from the authors on request. Contact: kramer@in.tum.de