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Fuzzy Knowledge-Based Subspace Clustering for Life Science Data Analysis
School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia / School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Penang, Malaysia.
Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan.ORCID iD: 0000-0002-4255-5130
School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia.
School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia.
2013 (English)In: Knowledge-Based Systems in Biomedicine and Computational Life Science / [ed] Tuan D. Pham and Lakhmi C. Jain, Springer Berlin/Heidelberg, 2013, 177-213 p.Chapter in book (Other academic)Text
Abstract [en]

Features or attributes play an important role when handling multi-dimensional datasets. Generally, not all the features are needed to find several groups of similar objects in traditional clustering methods because some of the features may not be relevant and also redundant. Hence, the concept of identifying subsets of the features that are relevant to clusters is introduced, instead of using the full set of features. This chapter discusses the use of the prior knowledge of the importance of features and their interaction in constructing both fuzzy measures and signed fuzzy measures for subspace clustering. The Choquet integral, which is known as a useful aggregation operator with respect to fuzzy measure, is used to aggregate the importance and interaction of the features. The concept of fuzzy knowledge-based subspace clustering is applied especially to the analysis of life science data in this chapter.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2013. 177-213 p.
Series
, Studies in Computational Intelligence, ISSN 1860-949X ; 450
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-125009DOI: 10.1007/978-3-642-33015-5_8ISBN: 978-3-642-33014-8 (Print)ISBN: 978-3-642-33015-5 (Online)OAI: oai:DiVA.org:liu-125009DiVA: diva2:902786
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-02-23Bibliographically approved

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