Data clustering is a popular unsupervised data mining tool that is used for partitioning a
given dataset into homogeneous groups based on some similarity/dissimilarity metric …

The aim of any data mining technique is to build an efficient predictive or descriptive model
of a large amount of data. Applications of evolutionary algorithms have been found to be …

Modern drug discovery involves the simultaneous optimization of many physicochemical
and biological properties that transcends the historical focus on bioactivity alone. The …

This paper is the second part of a two-part paper, which is a survey of multiobjective
evolutionary algorithms for data mining problems. In Part I, multiobjective evolutionary …

Clustering is an important unsupervised classification technique where data points are
grouped such that points that are similar in some sense belong to the same cluster. Cluster …

This is the first book primarily dedicated to clustering using multiobjective genetic algorithms
with extensive real-life applications in data mining and bioinformatics. The authors first offer …

DNA microarray is a powerful technology that can simultaneously determine the levels of
thousands of transcripts (generated, for example, from genes/miRNAs) across different …

Patient stratification has a major role in enabling efficient and personalized medicine. An
important task in patient stratification is to discover disease subtypes for effective treatment …

Clustering has been widely applied in interpreting the underlying patterns in microarray
gene expression profiles, and many clustering algorithms have been devised for the same …

DNA microarray data now permit scientists to screen thousand of genes simultaneously and
determine whether those genes are active or silent in normal and cancerous tissues. With …