Proceedings ArticleDOI
A new approach based on enhanced PSO with neighborhood search for data clustering
Dang Cong Tran,Zhijian Wu,Van Xuat Nguyen +2 more
- pp 98-104
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TLDR
An approach based on PSO and K-means is presented (denoted EPSO), in which PSO is enhanced by neighborhood search strategies, and outperforms some other data clustering algorithms, and has an acceptable efficiency and robustness.Abstract:
The well-known K-means algorithm has been successfully applied to many practical clustering problems, but it has some drawbacks such as local optimal convergence and sensitivity to initial points. Particle swarm optimization algorithm (PSO) is one of the swarm intelligent algorithms, it is applied in solving global optimization problems. An integration of enhanced PSO and K-means algorithm is becoming one of the popular strategies for solving clustering problems. In this study, an approach based on PSO and K-means is presented (denoted EPSO), in which PSO is enhanced by neighborhood search strategies. By hybrid with enhanced PSO, it does not only help the algorithm escape from local optima but also overcomes the shortcoming of the slow convergence speed of the PSO algorithm. Experimental results on eight benchmark data sets show that the proposed approach outperforms some other data clustering algorithms, and has an acceptable efficiency and robustness.read more
Citations
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[IEEE IEEE International Conference on Networking, Sensing and Control, 2004 - Taipei, Taiwan (March 21-23, 2004)] IEEE International Conference on Networking, Sensing and Control, 2004 - Particle swarm optimization algorithm and its application to clustering analysis
Ching-Yi Chen,Fun Ye +1 more
TL;DR: In this article, a particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed to search the cluster center in the arbitrary data set automatically, which can help the user to distinguish the structure of data and simplify the complexity of data from mass information.
Journal ArticleDOI
Swarm intelligence for clustering — A systematic review with new perspectives on data mining
Elliackin M. N. Figueiredo,Mariana Macedo,Hugo Siqueira,Clodomir J. Santana,Anu Gokhale,Carmelo J. A. Bastos-Filho +5 more
TL;DR: A systematic mapping review on recent investigations of swarm-inspired algorithms to tackle clustering problems and provides an overview of how to apply the swarm methods together with a critical analysis of the current and future perspectives in the field.
Journal ArticleDOI
Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks
TL;DR: Fuzzy and neural network prediction interval models are developed based on fuzzy numbers by minimizing a novel criterion that includes the coverage probability and normalized average width and show that the proposed models are suitable alternatives to electrical consumption forecasting because they obtain the minimum interval widths that characterize the uncertainty of this type of stochastic process.
Proceedings ArticleDOI
Prediction Interval Modeling Tuned by an Improved Teaching Learning Algorithm Applied to Load Forecasting in Microgrids
TL;DR: A linear and a Takagi-Sugeno fuzzy model are proposed and they are used to construct a prediction interval that includes a representation of the uncertainties and both models succeed in accomplish the design objectives.
Book ChapterDOI
D-PPSOK clustering algorithm with data sampling for clustering big data analysis
TL;DR: In this paper , the authors proposed a distributed-parallel particle swarm optimization with k-means (D-PPSOK) clustering algorithm with data sampling on large data sets for getting the clusters with less computational steps.
References
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Proceedings ArticleDOI
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TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
Journal ArticleDOI
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
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