Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing (original) (raw)
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2016
Pengelompokan adalah suatu teknik pelombongan data. Di dalam bidang set data tanpa selia, tugas mengelompok ialah dengan mengumpul set data kepada kelompok yang bermakna. Pengelompokan digunakan sebagai teknik penyelesaian di dalam pelbagai bidang dengan membahagikan dan mengstruktur semula data yang besar dan kompleks supaya menjadi lebih bererti justru mengubahnya kepada maklumat yang berguna. Clustering is a data mining technique. In the field of unsupervised datasets, the task of clustering is by grouping the dataset into meaningful clusters. Clustering is used as a data solution technique in various fields to divide and restructure the large and complex data to become more significant thus transform them into useful information
Multi-objective particle swarm optimization and simulated annealing in practice
Applied Mathematical Sciences, 2016
Several automatic clustering multi-objective algorithms have been proposed in literature to solve the clustering problem. Recently, Multi-Objective Particle Swarm Optimization and Simulated Annealing Algorithm (MOPSOSA) has been proposed. The aim of (MOPSOSA) is to estimate the appropriate number of clusters and appropriately partition a data set into these clusters without the need to know the actual number of clusters. In this work, the efficiency of the MOPSOSA algorithm is studied, which is based on parameters of particles' velocity. Some of the artificial and real-life datasets are used to illustrate the impact of velocity parameters in the efficiency of MOPSOSA algorithm. The 2088 Ahmad Abubaker et al. results show that the suitable values of velocity parameters have almost the same range for the datasets used during the experiments.
Restarted Simulated Annealing Particle Swarm Optimization used in Cluster Analysis
In order to solve the cluster analysis problem more efficiently, we presented a new approach based on Particle Swarm Optimization Sequence Quadratic Programming (RSAPSO). First, we created the optimization model using the variance ratio criterion (VRC) as fitness function. Second, RSAPSO was introduced to find the maximal point of the VRC. The experimental dataset contained 400 data of 4 groups with three different levels of overlapping degrees: non-overlapping, partial overlapping, and severely overlapping. We compared the RSAPSO with genetic algorithm (GA) and combinatorial particle swarm optimization (CPSO). Each algorithm was run 20 times. The results showed that RSAPSO could found the largest VRC values among all three algorithms, and meanwhile it cost the least time. It can conclude that RSAPSO is effective and rapid for the cluster analysis problem.
A new multiobjective simulated annealing based clustering technique using symmetry
Pattern Recognition Letters, 2009
In this paper, we have proposed a multiobjective clustering technique which optimizes simultaneously two objectives, one reflecting the total 'goodness' present in the data set in terms of total compactness (measured using Euclidean distance) of the clusters, and the other reflecting the total symmetry present in the clusters of the data set. The proposed algorithm uses a simulated annealing based multiobjective optimization method as the underlying optimization criterion. Center based encoding is used. The proposed multiobjective clustering technique is able to suitably evolve these cluster centers in such a way so that the two objectives are optimized 'simultaneously'. Assignment of points to different clusters is done based on the newly developed point symmetry based distance rather than the Euclidean distance. Results on eight artificial and six real-life data sets show that the proposed technique is well-suited to detect true partitioning from data sets with clusters having either the hyperspherical shape or point symmetric structure. Results are compared with those obtained by five existing clustering techniques, one multiobjective clustering technique, MOCK, average linkage clustering algorithm, expectation maximization clustering algorithm, well-known genetic algorithm based K-means clustering technique (GAK-means) and a newly developed genetic algorithm with point symmetry based clustering technique (GAPS).
A particle swarm optimization approach to clustering
Expert Systems with Applications, 2011
The clustering problem has been studied by many researchers using various approaches, including tabu searching, genetic algorithms, simulated annealing, ant colonies, a hybridized approach, and artificial bee colonies. However, almost none of these approaches have employed the pure particle swarm optimization (PSO) technique. This study presents a new PSO approach to the clustering problem that is effective, robust, comparatively efficient, easy-to-tune and applicable when the number of clusters is either known or unknown. The algorithm was tested using two artificial and five real data sets. The results show that the algorithm can successfully solve both clustering problems with both known and unknown numbers of clusters.
Scope of Research on Particle Swarm Optimization Based Data Clustering
ArXiv, 2019
Optimization is nothing but a mathematical technique which finds maxima or minima of any function of concern in some realistic region. Different optimization techniques are proposed which are competing for the best solution. Particle Swarm Optimization (PSO) is a new, advanced, and most powerful optimization methodology that performs empirically well on several optimization problems. It is the extensively used Swarm Intelligence (SI) inspired optimization algorithm used for finding the global optimal solution in a multifaceted search region. Data clustering is one of the challenging real world applications that invite the eminent research works in variety of fields. Applicability of different PSO variants to data clustering is studied in the literature, and the analyzed research work shows that, PSO variants give poor results for multidimensional data. This paper describes the different challenges associated with multidimensional data clustering and scope of research on optimizing t...
Clustering using Particle Swarm Optimization
2016
Data clustering has been a well-studied research field for a long time. One of the latest trends in this area is the application of Particle Swarm Optimization (PSO) in clustering which has good potential for improvements. This paper presents an approach to using Particle Swarm Optimization to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. Results show that PSO clustering techniques have much potential.
A Novel Data Clustering Algorithm based on Modified Adaptive Particle Swarm Optimization
International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016
Fuzzy clustering is a popular unsupervised learning method used in cluster analysis which allows a point in large data sets belongs to two or more clusters. Prior work suggests that Particle Swarm Optimization based approach could be a powerful tool for solving clustering problems. In this paper, we propose a data clustering algorithm based on modified adaptive particle swarm optimization. We choose to use artificial bee colony algorithm combined with PSO technique to modify the traditional clustering methods due to its fast convergence and the presence of adaptive mechanisms based on the evolutionary factor. On the one hand, Particle Swarm Optimization is proven to be an effective and robust technique for fuzzy clustering. On the other hand, the artificial bee colony algorithm has the capability to generate diversity within the swarm when the guide bees are in the exploration mode. Through numerical analysis and experimental simulation, we verify that our algorithm performs much better compared with other state-of-the-art algorithms. Future research schedule is also discussed in the final part.
Comparative Study of Particle Swarm Optimization based Unsupervised Clustering Techniques
2009
In order to overcome the shortcomings of traditional clustering algorithms such as local optima and sensitivity to initialization, a new Optimization technique, Particle Swarm Optimization is used in association with Unsupervised Clustering techniques in this paper. This new algorithm uses the capacity of global search in PSO algorithm and solves the problems associated with traditional clustering techniques. This merge avoids the local optima problem and increases the convergence speed. Parameters, time, distance and mean, are used to compare PSO based Fuzzy C-Means, PSO based Gustafson's-Kessel, PSO based Fuzzy K-Means with extragrades and PSO based K-Means are suitably plotted. Thus, Performance evaluation of Particle Swarm Optimization based Clustering techniques is achieved. Results of this PSO based clustering algorithm is used for remote image classification. Finally, accuracy of this image is computed along with its Kappa Coefficient.
A Hybrid Particle Swarm Optimization Algorithm for Clustering Analysis
Data Warehousing and …, 2007
This paper introduces a new approach called hybrid particle swarm optimization like algorithm (hybrid PSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimization like algorithm (ePSO). In order to accelerate the PSO algorithms to obtain the global optimal solution, three fine tuning operators, namely mutation, cross-over and root mean square variants are introduced. The effectiveness of the fine tuning elements with various PSO algorithms is tested through three benchmark functions along with a few recently developed state-of-the-art methods and the results are compared with those obtained without the fine tuning elements. From several comparative analyses, it is clearly seen that the performance of all the three PSO algorithms (pf-PSO, ePSO, and hybrid PSO) is considerably improved with various fine tuning operators and sometimes more competitive than the recently developed PSO algorithms.