A Differential Evolution-Based Clustering for Probability Density Functions
Clustering for probability density functions (CDFs) has recently emerged as a new interest technique in statistical pattern recognition because of its potential in various practical issues. For solving the CDF problems, evolutionary techniques which are successfully applied in clustering for discret...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8421225/ |
| _version_ | 1818617916714647552 |
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| author | Ho Kieu Diem Vo Duy Trung Nguyen Thoi Trung Vo Van Tai Nguyen Trang Thao |
| author_facet | Ho Kieu Diem Vo Duy Trung Nguyen Thoi Trung Vo Van Tai Nguyen Trang Thao |
| author_sort | Ho Kieu Diem |
| collection | DOAJ |
| description | Clustering for probability density functions (CDFs) has recently emerged as a new interest technique in statistical pattern recognition because of its potential in various practical issues. For solving the CDF problems, evolutionary techniques which are successfully applied in clustering for discrete elements have not been studied much in CDF. Therefore, this paper presents for the first time an application of the differential evolution (DE) algorithm for clustering of probability density functions (pdfs) in which the clustering problem is transformed into an optimization problem. In this optimization problem, the objective function is to minimize the internal validity measure-SF index, and the design variable is the name of the cluster in which pdfs are assigned to. To solve this optimization problem, a DE-based CDF is proposed. The efficiency and feasibility of the proposed approach are demonstrated through four numerical examples including analytical and real-life problems with gradually increasing the complexity of the problem. The obtained results mostly outperform several results of compared algorithms in the literature. |
| first_indexed | 2024-12-16T17:13:18Z |
| format | Article |
| id | doaj.art-773c01f56e8243498fc675961d013828 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:13:18Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-773c01f56e8243498fc675961d0138282022-12-21T22:23:21ZengIEEEIEEE Access2169-35362018-01-016413254133610.1109/ACCESS.2018.28496888421225A Differential Evolution-Based Clustering for Probability Density FunctionsHo Kieu Diem0Vo Duy Trung1Nguyen Thoi Trung2Vo Van Tai3Nguyen Trang Thao4https://orcid.org/0000-0003-2635-5371Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, VietnamDivision of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, VietnamDivision of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, VietnamCollege of Natural Science, Can Tho University, Can Tho, VietnamDivision of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, VietnamClustering for probability density functions (CDFs) has recently emerged as a new interest technique in statistical pattern recognition because of its potential in various practical issues. For solving the CDF problems, evolutionary techniques which are successfully applied in clustering for discrete elements have not been studied much in CDF. Therefore, this paper presents for the first time an application of the differential evolution (DE) algorithm for clustering of probability density functions (pdfs) in which the clustering problem is transformed into an optimization problem. In this optimization problem, the objective function is to minimize the internal validity measure-SF index, and the design variable is the name of the cluster in which pdfs are assigned to. To solve this optimization problem, a DE-based CDF is proposed. The efficiency and feasibility of the proposed approach are demonstrated through four numerical examples including analytical and real-life problems with gradually increasing the complexity of the problem. The obtained results mostly outperform several results of compared algorithms in the literature.https://ieeexplore.ieee.org/document/8421225/Clusteringdifferential evolution algorithmimage classificationprobability density functionSF-index |
| spellingShingle | Ho Kieu Diem Vo Duy Trung Nguyen Thoi Trung Vo Van Tai Nguyen Trang Thao A Differential Evolution-Based Clustering for Probability Density Functions IEEE Access Clustering differential evolution algorithm image classification probability density function SF-index |
| title | A Differential Evolution-Based Clustering for Probability Density Functions |
| title_full | A Differential Evolution-Based Clustering for Probability Density Functions |
| title_fullStr | A Differential Evolution-Based Clustering for Probability Density Functions |
| title_full_unstemmed | A Differential Evolution-Based Clustering for Probability Density Functions |
| title_short | A Differential Evolution-Based Clustering for Probability Density Functions |
| title_sort | differential evolution based clustering for probability density functions |
| topic | Clustering differential evolution algorithm image classification probability density function SF-index |
| url | https://ieeexplore.ieee.org/document/8421225/ |
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