Clustering analysis is applied generally to Pattern Recognition, Color Quantization and Image Classification. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. The user can understand the implied information behind extracting these data. In real case, the distribution of information can be any size and shape. A particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed in this article. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option of the Social-only model and Cognition-only model[1, 2, 3J. This method is quite simple and valid and it can avoid the minimum local value. Finally, the effectiveness of the PSO-clustering is demonstrated on four artificial data sets.

[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

Swarm intelligence for clustering — A systematic review with new perspectives on data mining

Prediction interval modelling has been proposed in the literature to characterize uncertain phenomena and provide useful information from a decision-making point of view. In most of the reported studies, assumptions about the data distribution are made and/or the models are trained at one step ahead, which can decrease the quality of the interval in terms of the information about the uncertainty modelled for a higher prediction horizon. In this paper, a new prediction interval modelling methodology based on fuzzy numbers is proposed to solve the abovementioned drawbacks. Fuzzy and neural network prediction interval models are developed based on this proposed methodology by minimizing a novel criterion that includes the coverage probability and normalized average width. The fuzzy number concept is considered because the affine combination of fuzzy numbers generates, by definition, prediction intervals that can handle uncertainty without requiring assumptions about the data distribution. The developed models are compared with a covariance-based prediction interval method, and high-quality intervals are obtained, as determined by the narrower interval width of the proposed method. Additionally, the proposed prediction intervals are tested by forecasting up to two days ahead of the load of the Huatacondo microgrid in the north of Chile and the consumption of the residential dwellings in the town of Loughborough, UK. The results 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. Furthermore, the information provided by the obtained prediction interval could be used to develop robust energy management systems that, for example, consider the worst-case scenario.

https://repository.tudelft.nl/islandora/object/uuid%3A4aaea97d-fd74-4ae0-b150-974d2592a21f/datastream/OBJ/download

Prediction interval methodology based on fuzzy numbers and its extension to fuzzy systems and neural networks

Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation

Clustering is a technique which is used to group the data into different subgroups or subsets to retrieve meaningful information from the available huge dataset. The trending swarm based intelligent system replaces the conventional clustering algorithm with the objective of increased performance. Ant lion optimization (ALO) technique is the swarm based intelligence that exhibits the hunting mechanism of the ant lions in the natural environment. Ant colony optimization (ACO) algorithm is a swarm based intelligence technique which inherits the behaviour of natural ant. In this paper new hybrid ACO–ALO algorithm was proposed to solve the data clustering problem. Additionally Cauchy’s mutation operator is added with this proposed algorithm to avoid the local minima trapping problem. The main objective is to reduce the intra cluster distance in clustering problem. From the experimental analysis, it evidences the proposed ACO–ALO algorithm outperforms the traditional algorithms of data clustering.

Hybrid metaheuristic algorithm for improving the efficiency of data clustering

Fuzzy C-means algorithms (FCMs) incorporating local information has been widely used for image segmentation, especially on image corrupted by noise. However, they cannot obtain the satisfying segmentation performance on the image heavily contaminated by noise, sensitivity to initial points, and can be trapped into local optima. Hence, optimization techniques are often used in conjunction with algorithms to improve the performance. In this paper, Particle Swarm Optimization (PSO) is introduced into fast generalized FCM (FGFCM) incorporating with local spatial and gray information called PFGFCM, where the membership degree values were modified by applying optimal-selection-based suppressed strategy. Experimental results on synthetic and real images heavily corrupted by noise show that the proposed method is superior to other fuzzy algorithms.

Fast Generalized Fuzzy C-means Using Particle Swarm Optimization for Image Segmentation

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.

A new approach based on enhanced PSO with neighborhood search for data clustering

K-means is one of the most popular clustering algorithm, it has been successfully applied in solving many practical clustering problems, however there exist some drawbacks such as local optimal convergence and sensitivity to initial points In this paper, a new approach based on enhanced particle swarm optimization (PSO) is presented (denoted CMPNS), in which PSO is enhanced by new neighborhood search strategy and Cauchy mutation operation Experimental results on fourteen used artificial and real-world datasets show that the proposed method outperforms than that of some other data clustering algorithms in terms of accuracy and convergence speed

Data Clustering Based on Particle Swarm Optimization with Neighborhood Search and Cauchy Mutation

Like other stochastic algorithms, particle swarm optimization algorithm (PSO) has shown a good performance over global numerical optimization However, PSO also has a few drawbacks such as premature convergence and low convergence speed, especially on complex problem In this paper, we present a new approach called AMPSONS in which neighborhood search, diversity mechanism and adaptive mutation were utilized Experimental results obtained from a test on several benchmark functions showed that the performance of proposed AMPSONS algorithm is superior to five other PSO variants, namely CLPSO, AMPSO, GOPSO, DNLPSO, and DNSPSO, in terms of convergence speed and accuracy

A New Approach of Diversity Enhanced Particle Swarm Optimization with Neighborhood Search and Adaptive Mutation

In this paper, by combination of some approaches we propose a new approach of Differential Evolution (DE) algorithm, called DE with nonlinear simplex method and dynamic neighborhood search (DENNS). In our approach the nonlinear simplex method (NSM) is used for population initialization and local neighborhood search. Moreover, local and global neighborhood search operators are employed to generate high quality candidate solutions. During the search process, the population is periodically ranked to change the topology of neighbors. Experimental studies are conducted on a comprehensive set of benchmark functions. Simulation results show that DENNS achieves better results on the majority of test functions, when comparing with some other similar evolutionary algorithms.

Dang Cong Tran

Papers

Fast Generalized Fuzzy C-means Using Particle Swarm Optimization for Image Segmentation

A new approach based on enhanced PSO with neighborhood search for data clustering

Data Clustering Based on Particle Swarm Optimization with Neighborhood Search and Cauchy Mutation

A New Approach of Diversity Enhanced Particle Swarm Optimization with Neighborhood Search and Adaptive Mutation

Differential evolution with nonlinear simplex method and dynamic neighborhood search