Background Biclustering is a critical task for biomedical applications. Order-preserving
biclusters, submatrices where the values of rows induce the same linear ordering across …

The main motivation for using a multi-objective evolutionary algorithm for finding biclusters
in gene expression data is motivated by the fact that when looking for biclusters in gene …

The detection of correlations between different features in high dimensional data sets is a
very important data mining task. These correlations can be arbitrarily complex: one or more …

Biclustering analysis is a useful methodology to discover the local coherent patterns hidden
in a data matrix. Unlike the traditional clustering procedure, which searches for groups of …

Biclustering is becoming a popular technique for the study of gene expression data. This is
mainly due to the capability of biclustering to address the data using various dimensions …

In this thesis we study the Bicluster Graph Editing Problem. The goal is to add/remove fewest
edges from a bipartite graph so that it becomes a vertex disjoint union of complete bipartite …

Traditional similarity or distance measurements usually become meaningless when the
dimensions of the datasets increase, which has detrimental effects on clustering …

In data sets some attributes may have higher or lower importance. One of the tools used for
data analysis of such datasets are subspace neuro-fuzzy systems. They elaborate fuzzy …

Clustering techniques often define the similarity between instances using distance
measures over the various dimensions of the data [12, 14]. Subspace clustering is an …

Motivation: Analysis of gene expression data can provide insights into the positive and
negative co-regulation of genes. However, existing methods such as association rule mining …