Attribute clustering for grouping, selection, and classification of gene expression

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proposed approach, we applied it to two well-known gene expression datasets and compared our results with those obtained by other methods. Our experiments show that the proposed method is able to find the meaningful clusters of genes. By selecting a subset of genes which have high multiple-interdependence with others within clusters, significant classification information can be obtained. Thus a small pool of selected genes can be used to build classifiers with very high classification rate. From the pool, gene expressions of different categories can be identified.
Abstract
This paper presents an attribute clustering method which is able to group genes based on their interdependence so as to mine meaningful patterns from the gene expression data. It can be used for gene grouping, selection and classification. The partitioning of a relational table into attribute subgroups allows a small number of attributes within or across the groups to be selected for analysis. By clustering attributes, the search dimension of a data mining algorithm is reduced. The reduction of search dimension is especially important to data mining in gene expression data because such data typically consist of a huge number of genes (attributes) and a small number of gene expression profiles (tuples). Most data mining algorithms are typically developed and optimized to scale to the number of tuples instead of the number of attributes. The situation becomes even worse when the number of attributes overwhelms the number of tuples, in which case, the likelihood of reporting patterns that are actually irrelevant due to chances becomes rather high. It is for the aforementioned reasons that gene grouping and selection are important preprocessing steps for many data mining algorithms to be effective when applied to gene expression data. This paper defines the problem of attribute clustering and introduces a methodology to solving it. Our proposed method groups interdependent attributes into clusters by optimizing a criterion function derived from an information measure that reflects the interdependence between attributes. By applying our algorithm to gene expression data, meaningful clusters of genes are discovered. The grouping of genes based on attribute interdependence within group helps to capture different aspects of gene association patterns in each group. Significant genes selected from each group then contain useful information for gene expression classification and identification. To evaluate the performance of the
Index Terms: Data mining, attribute clustering, gene selection, gene expression classification, microarray analysis.
1 Introduction
Clustering is an important topic in data mining research. Given a relational table, a conventional clustering algorithm groups tuples, each of which is characterized by a set of attributes, into clusters based on similarity [27]. Intuitively, tuples in a cluster are more similar to each other than those belonging to different clusters. It has been shown that clustering is very useful in many data mining applications (e.பைடு நூலகம்., [22], [46]).
* Corresponding author Manuscript received Sep. 15, 2004; revised Dec. 1, 2004; accepted March 1, 2005. The work by W.-H. Au and K. C. C. Chan was supported in part by The Hong Kong Polytechnic University under Grants A-P209 and G-V958. W.-H. Au and K. C. C. Chan are with the Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (e-mail: whau@; cskcchan@.hk). A. K. C. Wong is with the Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada (e-mail: akcwong@pami.uwaterloo.ca). Y. Wang is with Pattern Discovery Software Systems, Ltd., Waterloo, Ontario N2L 5Z4, Canada (e-mail: yang.wang@).
Attribute Clustering for Grouping, Selection, and
Classification of Gene Expression Data
Wai-Ho Au*, Member, IEEE, Keith C. C. Chan, Andrew K. C. Wong, Fellow, IEEE, and Yang Wang, Member, IEEE
When applied to gene expression data analysis, conventional clustering algorithms often encounter the problem related to the nature of gene expression data which is normally “wide” and “shallow.” In another words, data sets usually contain a huge number of genes (attributes) and a small number of gene expression profiles (tuples). This characteristic of gene expression data often compromises the performance of conventional clustering algorithms. In this paper, we present a methodology to group attributes that are interdependent or correlated with each other. We refer to such a process as attribute clustering. In this sense, attributes in a cluster are more correlated with each other whereas attributes in different clusters are less correlated. Attribute clustering is able to reduce the search dimension of a data mining algorithm to effectuate the search of interesting relationships or for construction of models in a tightly correlated subset of attributes rather than in the entire attribute space. After attributes are clustered, one can select a smaller number for further analysis.