Abstract—A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications. Index Terms—Biclustering, simultaneous clustering, coclustering, subspace clustering, bidimensional clustering, direct clustering, block clustering, two-way clustering, two-mode clustering, two-sided clustering, microarray data analysis, biological data analysis, gene expression data. 1

Abstract—It is a consensus in microarray analysis that identifying potential local patterns, characterized by coherent groups of genes and conditions, may shed light on the discovery of previously undetectable biological cellular processes of genes, as well as macroscopic phenotypes of related samples. In order to simultaneously cluster genes and conditions, we have previously developed a fast coclustering algorithm, Minimum Sum-Squared Residue Coclustering (MSSRCC), which employs an alternating minimization scheme and generates what we call coclusters in a “checkerboard ” structure. In this paper, we propose specific strategies that enable MSSRCC to escape poor local minima and resolve the degeneracy problem in partitional clustering algorithms. The strategies include binormalization, deterministic spectral initialization, and incremental local search. We assess the effects of various strategies on both synthetic gene expression data sets and real human cancer microarrays and provide empirical evidence that MSSRCC with the proposed strategies performs better than existing coclustering and clustering algorithms. In particular, the combination of all the three strategies leads to the best performance. Furthermore, we illustrate coherence of the resulting coclusters in a checkerboard structure, where genes in a cocluster manifest the phenotype structure of corresponding specific samples and evaluate the enrichment of functional annotations in Gene Ontology (GO). Index Terms—Microarray analysis, coclustering, binormalization, deterministic spectral initialization, local search, gene ontology. 1

The user-assigned tag is a growingly important research topic in MIR. Noticing that some tags are more specific versions of others, this paper studies the problem of organizing tags into a hierarchical structure by taking into account the fact that the corresponding artists are organized into a hierarchy based on genre and style. A novel clustering algorithm, Hierarchical Co-clustering Algorithm (HCC), is proposed as a solution. Unlike traditional hierarchical clustering algorithms that deal with homogeneous data only, the proposed algorithm simultaneously organizes two distinct data types into hierarchies. HCC is additionally able to receive constraints that state certain objects “must-be-together ” or “should-be-together ” and build clusters so as to satisfying the constraints. HCC may lead to better and deeper understandings of relationship between artists and tags assigned to them. An experiment finds that by trying to hierarchically cluster the two types of data better clusters are obtained for both. It is also shown that HCC is able to incorporate instance-level constraints on artists and/or tags to improve the clustering process. 1.