We consider the problem of identifying a small sub-matrix of activation in a large noisy matrix. We establish the minimax rate for the problem by showing tight (up to constants) upper and lower bounds on the signal strength needed to identify the sub-matrix. We consider several natural computationally tractable procedures and show that under most parameter scalings they are unable to identify t...

Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two algorithms have been proposed that specifically deal with binary matrices. None of the gene expression bi...

A recommender system aims to recommend items that a user is interested in among many items. The need for the recommender system has been expanded by the information explosion. Various approaches have been suggested for providing meaningful recommendations to users. One of the proposed approaches is to consider a recommender system as aMarkov decision process (MDP) problem and try to solve it us...

The advent of high-throughput measurement technologies in molecular biology enabled the determination of cellular parameters like the concentration of proteins, mRNAs or metabolites or the binding between molecules on a genome scale. The resulting data make new types of analyses possible which focus more on interactions between multiple elements such as genes, proteins or metabolites. A promine...

Clustering is a very powerful tool for automatic detection of relevant sub-groups in unlabeled data sets. It can be sometime very interesting to be able to regroup and visualize the attributes used to describe the data, in addition to the clustering of these data. In this paper, we propose a coclustering algorithm based on the learning of a Self Organizing Map. The new algorithm will thus be ab...

The validation of biclustering algorithms remains a challenging task, even though a number of measures have been proposed for evaluating the quality of these algorithms. Although no criterion is universally accepted as the overall best, a number of meta-evaluation conditions to be satisfied by biclustering algorithms have been enunciated. In this work, we present MOCICE-BCubed F1, a new externa...

In this chapter, different methods and applications of biclustering algorithms to DNA microarray data analysis that have been developed in recent years are discussed and compared. Identification of biological significant clusters of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Various computational...

In this paper, a new algorithm fuzzy co-clustering with Ruspini s condition (FCR) is proposed for co-clustering documents and words. Compared to most existing fuzzy co-clustering algorithms, FCR is able to generate fuzzy word clusters that capture the natural distribution of words, which may be beneficial for information retrieval. We discuss the principle behind the algorithm through some theo...