Applying fuzzy measures and nonlinear integrals in data mining

Authors:
Zhenyuan Wang;Kwong-Sak Leung;George J. Klir
Affiliations:
Department of Mathematics, University of Nebraska at Omaha, Omaha, NE 68182, USA;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong;Department of Systems Science and Industrial Engineering, Thomas J. Watson School of Engineering, Binghamton University-SUNY, Binghamton, NY 13902, USA
Venue:
Fuzzy Sets and Systems
Year:
2005

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Abstract

The paper gives an overview of applying fuzzy measures and relevant nonlinear integrals in data mining, discussed in five application areas: set function identification, nonlinear multiregression, nonlinear classification, networks, and fuzzy data analysis. In these areas, fuzzy measures allow us to describe interactions among feature attributes towards a certain target (objective attribute), while nonlinear integrals serve as aggregation tools to combine information from feature attributes. Values of fuzzy measures in these applications are unknown and are optimally determined via a soft computing technique based on given data.