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Chapter 6
Recent Advances of Data Biclustering with
Application in Computational Neuroscience
Neng Fan, Nikita Boyko, and Panos M. Pardalos
Abstract Clustering and biclustering are important techniques arising in data min-
ing. Different from clustering, biclustering simultaneously groups the objects and
features according their expression levels. In this review, the backgrounds, moti-
vation, data input, objective tasks, and history of data biclustering are carefully
studied. The bicluster types and biclustering structures of data matrix are defined
mathematically. Most recent algorithms, including OREO, nsNMF, BBC, cMonkey,
etc., are reviewed with formal mathematical models. Additionally, a match score be-
tween biclusters is defined to compare algorithms. The application of biclustering
in computational neuroscience is also reviewed in this chapter.
6.1 Introduction
6.1.1 Motivation
With the number of database appearing in computational biology, biomedical en-
gineering, consumers' behavior survey, and social networks, finding the useful in-
formation behind these data and grouping the data are important issues nowadays.
Clustering is a method to classify the objects into different groups, so that the
objects in each group share some common traits [15, 31, 57]. After this step, the data
 
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