An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering
This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/6123874 |
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doaj-fdaf122f76ac4a62a8bbfa9a2061a49b2020-11-24T22:57:28ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/61238746123874An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data ClusteringZe Dong0Hao Jia1Miao Liu2Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, ChinaHebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, ChinaHebei Engineering Research Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, ChinaThis paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.http://dx.doi.org/10.1155/2018/6123874 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ze Dong Hao Jia Miao Liu |
spellingShingle |
Ze Dong Hao Jia Miao Liu An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering Mathematical Problems in Engineering |
author_facet |
Ze Dong Hao Jia Miao Liu |
author_sort |
Ze Dong |
title |
An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering |
title_short |
An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering |
title_full |
An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering |
title_fullStr |
An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering |
title_full_unstemmed |
An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering |
title_sort |
adaptive multiobjective genetic algorithm with fuzzy c-means for automatic data clustering |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2018-01-01 |
description |
This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved. |
url |
http://dx.doi.org/10.1155/2018/6123874 |
work_keys_str_mv |
AT zedong anadaptivemultiobjectivegeneticalgorithmwithfuzzycmeansforautomaticdataclustering AT haojia anadaptivemultiobjectivegeneticalgorithmwithfuzzycmeansforautomaticdataclustering AT miaoliu anadaptivemultiobjectivegeneticalgorithmwithfuzzycmeansforautomaticdataclustering AT zedong adaptivemultiobjectivegeneticalgorithmwithfuzzycmeansforautomaticdataclustering AT haojia adaptivemultiobjectivegeneticalgorithmwithfuzzycmeansforautomaticdataclustering AT miaoliu adaptivemultiobjectivegeneticalgorithmwithfuzzycmeansforautomaticdataclustering |
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1725650634674274304 |