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Fuzzy Knowledge-Based Subspace Clustering for Life Science Data Analysis
The University of New South Wales, Canberra, ACT 2600, Australia /Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia.
Professor and Leader of the Aizu Research Cluster for Medical Engineering and Informatics and Professor of Medical Image Processing at the University of Aizu, Japan.ORCID iD: 0000-0002-4255-5130
The University of New South Wales, Canberra, ACT 2600, Australia .
The University of New South Wales, Canberra, ACT 2600, Australia .
2013 (English)In: Knowledge-Based Systems in Biomedicine and Computational Life Science / [ed] Tuan D. Pham, 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.
, Studies in Computational Intelligence, ISSN 1860-949X ; 450
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-127919DOI: 10.1007/978-3-642-33015-5_8ISBN: 978-3-642-33014-8ISBN: 978-3-642-33015-5OAI: diva2:927947
Available from: 2016-05-13 Created: 2016-05-13 Last updated: 2016-05-20

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Computer Vision and Robotics (Autonomous Systems)

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