We present a framework for biclustering and clustering where the observations are general labels. Our approach is based on the maximum likelihood estimator and its convex relaxation, and generalizes recent works in graph clustering to the biclustering setting. In addition to standard biclustering setting where one seeks to discover clustering structure simultaneously in two domain sets, we show...

Biclustering numerical data became a popular data-mining task in the beginning of 2000’s, especially for analysing gene expression data. A bicluster reflects a strong association between a subset of objects and a subset of attributes in a numerical object/attribute data-table. So called biclusters of similar values can be thought as maximal sub-tables with close values. Only few methods address...

Biclustering is the analog of clustering on a bipartite graph. Existent methods infer biclusters through local search strategies that find one cluster at a time; a common technique is to update the row memberships based on the current column memberships, and vice versa. We propose a biclustering algorithm that maximizes a global objective function using message passing. Our objective function c...

The biclustering, co-clustering, or subspace clustering problem involves simultaneously grouping the rows and columns of a data matrix to uncover biclusters or sub-matrices of the data matrix that optimize a desired objective function. In coherent biclustering, the objective function contains a coherence measure of the biclusters. We introduce a novel formulation of the coherent biclustering pr...

Motivation: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness h...

This chapter focuses on recommender systems based on the coherent user’s browsing patterns. Biclustering approach is used to discover the aggregate usage profiles from the preprocessed Web data. A combination of Discrete Artificial Bees Colony Optimization and Simulated Annealing technique is used for optimizing the aggregate usage profiles from the preprocessed clickstream data. Web page recom...

As we have seen in previous lectures, the technology of DNA chips allows the measurement of mRNA levels simultaneously for thousands of genes. The results of DNA chip experiments are usually organized together in a gene expression matrix, with rows corresponding to genes and columns corresponding to conditions. A bicluster can be defined as a submatrix spanned by a subset of genes and a subset ...

The increasing availability of gene expression data has encouraged the development of purposely-built intelligent data analysis techniques. Grouping genes characterized by similar expression patterns is a widespread accepted – and often mandatory – analysis step. Despite the fact that a number of biclustering methods have been developed to discover clusters of genes exhibiting a similar express...

In this paper, the Simulated Annealing (SA) based biclustering approach is proposed in which SA is used as an optimization tool for biclustering of web usage data to identify the optimal user profile from the given web usage data. Extracted biclusters are consists of correlated users whose usage behaviors are similar across the subset of web pages of a web site where as these users are uncorrel...

Biclustering numerical data became a popular datamining task in the beginning of 2000’s, especially for analysing gene expression data. A bicluster reflects a strong association between a subset of objects and a subset of attributes in a numerical object/attribute data-table. So called biclusters of similar values can be thought as maximal sub-tables with close values. Only few methods address ...