Recent advances of data biclustering with application in computational neuroscience

Neng Fan, Nikita Boyko, Panos M. Pardalos

Research output: Chapter in Book/Report/Conference proceedingChapter

10 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer International Publishing
Pages85-112
Number of pages28
Volume38
DOIs
StatePublished - 2010
Externally publishedYes

Publication series

NameSpringer Optimization and Its Applications
Volume38
ISSN (Print)19316828
ISSN (Electronic)19316836

ASJC Scopus subject areas

  • Control and Optimization

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  • Cite this

    Fan, N., Boyko, N., & Pardalos, P. M. (2010). Recent advances of data biclustering with application in computational neuroscience. In Springer Optimization and Its Applications (Vol. 38, pp. 85-112). (Springer Optimization and Its Applications; Vol. 38). Springer International Publishing. https://doi.org/10.1007/978-0-387-88630-5_6