Title :
Constructing fuzzy measures: a new method and its application to cluster analysis
Author :
Yuan, Bo ; Klir, George J.
Author_Institution :
Dept. of Syst. Sci. & Ind. Eng., State Univ. of New York, Binghamton, NY, USA
Abstract :
We first prove that for a given set of data there exists a fuzzy measure fitting exactly the data if and only if there exists an exact solution of the associated fuzzy relation equation. Secondly, we continue to study the special neural network we proposed in Proc. IFSA´95 World Congress, pp. 61-64 (1995), and describe a learning algorithm for obtaining an approximate fuzzy measure when no one exactly fits the data. Finally, we propose a clustering method based on fuzzy measures and integrals. A benchmark data set, the well-known Iris data set, is adopted to illustrate the method
Keywords :
data analysis; fuzzy set theory; learning (artificial intelligence); neural nets; pattern recognition; Iris data set; approximate fuzzy measure; benchmark data set; cluster analysis; data fitting; fuzzy integrals; fuzzy measures construction; fuzzy relation equation; learning algorithm; neural network; nonadditive measures; Clustering algorithms; Fitting; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Industrial engineering; Integral equations; Intelligent systems; Neural networks; Power measurement;
Conference_Titel :
Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American
Conference_Location :
Berkeley, CA
Print_ISBN :
0-7803-3225-3
DOI :
10.1109/NAFIPS.1996.534798
