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...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.41325-41336
Main Authors: Diem, Ho Kieu, Trung, Vo Duy, Trung, Nguyen Thoi, Van Tai, Vo, Thao, Nguyen Trang
Format: Article
Language:English
Subjects:
Clustering
Clustering algorithms
differential evolution algorithm
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
image classification
Indexes
Linear programming
Mathematical analysis
Optimization
Pattern recognition
Probability density function
Probability density functions
SF-index
Statistical analysis
Urban areas
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2849688