Recent Advances of Data Biclustering with Application in Computational Neuroscience

Neng Fan (), Nikita Boyko () and Panos M. Pardalos ()
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Neng Fan: University of Florida
Nikita Boyko: University of Florida
Panos M. Pardalos: University of Florida

Chapter Chapter 6 in Computational Neuroscience, 2010, pp 85-112 from Springer

Abstract: Abstract Clustering and biclustering are important techniques arising in data mining. Different from clustering, biclustering simultaneously groups the objects and features according their expression levels. In this review, the backgrounds, motivation, 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 between biclusters is defined to compare algorithms. The application of biclustering in computational neuroscience is also reviewed in this chapter.

Keywords: Lyapunov Exponent; Bipartite Graph; Data Matrix; Vagus Nerve Stimulation; Nonnegative Matrix Factorization (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-0-387-88630-5_6

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