Biclustering has proved to be a powerful data analysis technique due to its wide success in various application domains. However, the existing literature presents efficient solutions only for enumerating maximal biclusters with constant values, or heuristic-based approaches which can not find all biclusters or even support the maximality of the obtained biclusters. Here, we present a general fa...

Collaborative Filtering (CF) Systems have been studied extensively for more than a decade to confront the “information overload” problem. Nearest-neighbor CF is based either on common user or item similarities, to form the user’s neighborhood. The effectiveness of the aforementioned approaches would be augmented, if we could combine them. In this paper, we use biclustering to disclose this dual...

The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perpective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expre...

Biclustering is used for discovering correlations among subsets of attributes with subsets of transactions in a transaction database. It has an extensive set of applications ranging from Gene co-regulation analysis[4], documentkeyword clustering[12] and collaborative filtering for online recommendation systems[13]. In this paper, we propose optimal biclustering problem as maximal crossing numbe...

SUMMARY Besides classical clustering methods such as hierarchical clustering, in recent years biclustering has become a popular approach to analyze biological data sets, e.g. gene expression data. The Biclustering Analysis Toolbox (BicAT) is a software platform for clustering-based data analysis that integrates various biclustering and clustering techniques in terms of a common graphical user i...

As websites increase in complexity, locating needed information becomes a difficult task. Such difficulty is often related to the websites’ design but also ineffective and inefficient navigation processes. Research in web mining addresses this problem by applying techniques from data mining and machine learning to web data and documents. In this study, the authors examine web usage mining, appl...

Cheng and Church proposed a greedy deletion-addition algorithm to find a given number of k biclusters, whose mean squared residues (MSRs) are below certain thresholds and the missing values in the matrix are replaced with random numbers. In our previous paper we introduced the dual biclustering method with quadratic optimization to missing data and row inversions. In this paper, we modified the...

Background. Biclustering algorithms for the analysis of high-dimensional gene expression data were proposed. Among them, the plaid model is arguably one of the most flexible biclustering models up to now. Objective. The main goal of this study is to provide an evaluation of plaid models. To that end, we will investigate this model on both simulation data and real gene expression datasets. Metho...

Biclustering is an important problem that arises in diverse applications, including the analysis of gene expression and drug interaction data. A large number of clustering approaches have been proposed for 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 exi...

Many of the biclustering problems have been shown to be NP-complete. However, when they are interested in identify biclusters in time series expression data, it can limit the problem by finding only maximal biclusters with contiguous columns. This restriction leads to a well-mannered problem. Its motivation is the fact that biological processes start and conclude in an identifiable contiguous p...