document: An association analysis approach to biclustering The discovery of biclusters , which denote groups of items that show coherent values across a subset of all the transactions in a data set , is an important type of analysis performed on real-valued data sets in various domains , such as biology . Several algorithms have been proposed to find different types of biclusters in such data sets . However , these algorithms are unable to search the space of all possible biclusters exhaustively . Pattern mining algorithms in association analysis also essentially produce biclusters as their result , since the patterns consist of items that are supported by a subset of all the transactions . However , a major limitation of the numerous techniques developed in association analysis is that they are only able to analyze data sets with binary and\/or categorical variables , and their application to real-valued data sets often involves some lossy transformation such as discretization or binarization of the attributes . In this paper , we propose a novel association analysis framework for exhaustively and efficiently mining `` range support '' patterns from such a data set . On one hand , this framework reduces the loss of information incurred by the binarization - and discretization-based approaches , and on the other , it enables the exhaustive discovery of coherent biclusters . We compared the performance of our framework with two standard biclustering algorithms through the evaluation of the similarity of the cellular functions of the genes constituting the patterns\/biclusters derived by these algorithms from microarray data . These experiments show that the real-valued patterns discovered by our framework are better enriched by small biologically interesting functional classes . Also , through specific examples , we demonstrate the ability of the RAP framework to discover functionally enriched patterns that are not found by the commonly used biclustering algorithm ISA . The source code and data sets used in this paper , as well as the supplementary material , are available at http:\/\/www.cs.umn.edu\/vk\/gaurav\/rap .
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