Microarray gene expression data generally suffers from missing value problem due to a
variety of experimental reasons. Since the missing data points can adversely affect …

The Affymetrix GeneChip system is one of the most widely adapted microarray platforms.
However, due to the overwhelming amount of information available, many Affymetrix users …

Background In a number of domains, like in DNA microarray data analysis, we need to
cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify …

In this paper, we present a novel method for co-clustering, an unsupervised learning
approach that aims at discovering homogeneous groups of data instances and features by …

DNA microarray experiment inevitably generates gene expression data with missing values.
An important and necessary pre-processing step is thus to impute these missing values …

Exploring data sets by applying biclustering algorithms was first introduced in gene
expression analysis. While the generated biclustered data grows with increasing rates due …

Road network interruptions caused by natural disasters are becoming more frequent and
their consequences are becoming of a wider range. The main goal of this work is to identify …

Background Cluster analysis is widely used to discover patterns in multi-dimensional data.
Clustered heatmaps are the standard technique for visualizing one-way and two-way …

Historical financial data are frequently used in technical analysis to identify patterns that can
be exploited to achieve trading profits. Although technical analysis using a variety of …

Background Biclustering aims at finding subgroups of genes that show highly correlated
behaviors across a subgroup of conditions. Biclustering is a very useful tool for mining …