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 associat...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Gaurav Pandey, Gowtham Atluri, Michael Steinbach, Chad L. Myers, Vipin Kumar
• Keywords: Association Analysis Framework, Data Mining, Data Sets, KDD 2009, Real-valued Data Sets