Multistage random sampling genetic-algorithm-based fuzzy c-means clustering algorithm

This work presents a multistage random sampling genetic-algorithm-based fuzzy c-means clustering algorithm (called GMRFCM), which can significantly reduce the iterative times required to converge, the sensitivity to the initialization, and can obtain a better partition of a data set into c classes....

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Vydáno v:Proceedings of 2004 International Conference on Machine Learning and Cybernetics : August 6-29, 2004, Worldfield Convention Hotel, Shanghai, China Ročník 4; s. 2069 - 2073 vol.4
Hlavní autoři: Yun-Ying Dong, Yun-Jie Zhang, Chun-Ling Chang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 2004
Témata:
Biological cells
Clustering algorithms
Convergence
Fuzzy sets
Genetic mutations
Iterative algorithms
Mathematics
Partitioning algorithms
Physics
Sampling methods
ISBN:0780384032, 9780780384033
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Abstract This work presents a multistage random sampling genetic-algorithm-based fuzzy c-means clustering algorithm (called GMRFCM), which can significantly reduce the iterative times required to converge, the sensitivity to the initialization, and can obtain a better partition of a data set into c classes. At the first of this algorithm, it uses the multistage random sampling fuzzy c-means clustering algorithm (MRFCM) to produce an initial population; then applies this population on the improved fuzzy genetic cluster algorithm (GFGA) to perform genetic operations. In this way, the proposed algorithm in this paper can have strong global and local searching capability and it is especially significant for high-dimensional and large data sets. Experiments are given in the last of this paper. It is observed that the proposed algorithm in this paper searches better than MRFCM in the iterative times required to converge and the final objective function value.
AbstractList This work presents a multistage random sampling genetic-algorithm-based fuzzy c-means clustering algorithm (called GMRFCM), which can significantly reduce the iterative times required to converge, the sensitivity to the initialization, and can obtain a better partition of a data set into c classes. At the first of this algorithm, it uses the multistage random sampling fuzzy c-means clustering algorithm (MRFCM) to produce an initial population; then applies this population on the improved fuzzy genetic cluster algorithm (GFGA) to perform genetic operations. In this way, the proposed algorithm in this paper can have strong global and local searching capability and it is especially significant for high-dimensional and large data sets. Experiments are given in the last of this paper. It is observed that the proposed algorithm in this paper searches better than MRFCM in the iterative times required to converge and the final objective function value.
Author Yun-Ying Dong
Chun-Ling Chang
Yun-Jie Zhang
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  surname: Yun-Jie Zhang
  fullname: Yun-Jie Zhang
  organization: Dept. of Math. & Phys., Dalian Maritime Univ., China
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  surname: Chun-Ling Chang
  fullname: Chun-Ling Chang
  organization: Dept. of Math. & Phys., Dalian Maritime Univ., China
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Snippet This work presents a multistage random sampling genetic-algorithm-based fuzzy c-means clustering algorithm (called GMRFCM), which can significantly reduce the...
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StartPage 2069
SubjectTerms Biological cells
Clustering algorithms
Convergence
Fuzzy sets
Genetic mutations
Iterative algorithms
Mathematics
Partitioning algorithms
Physics
Sampling methods
Title Multistage random sampling genetic-algorithm-based fuzzy c-means clustering algorithm
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