A particle swarm optimization approach to clustering

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.

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...

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.

Swarm and Evolutionary Computation, 2014

Optimization based pattern discovery has emerged as an important field in knowledge discovery and data mining (KDD), and has been used to enhance the efficiency and accuracy of clustering, classification, association rules and outlier detection. Cluster analysis, which identifies groups of similar data items in large datasets, is one of its recent beneficiaries. The increasing complexity and large amounts of data in the datasets have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. Swarm intelligence (SI) is one such optimization technique whose algorithms have successfully been demonstrated as solutions for different data clustering domains. In this paper we investigate the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO). This paper makes two major contributions. Firstly, it provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering. Secondly, we analyze the reported results and highlight the performance of different techniques against contemporary clustering techniques. We also provide an brief overview of our PSO-based hierarchical clustering approach (HPSO-clustering) and compare the results with traditional hierarchical agglomerative clustering (HAC), K-means, and PSO clustering.

Journal of Intelligent Systems, 2017

Clustering is an unsupervised kind of grouping of data points based on the similarity that exists between them. This paper applied a combination of particle swarm optimization and K-means for data clustering. The proposed approach tries to improve the performance of traditional partition clustering techniques such as K-means by avoiding the initial requirement of number of clusters or centroids for clustering. The proposed approach is evaluated using various primary and real-world datasets. Moreover, this paper also presents a comparison of results produced by the proposed approach and by the K-means based on clustering validity measures such as inter- and intra-cluster distances, quantization error, silhouette index, and Dunn index. The comparison of results shows that as the size of the dataset increases, the proposed approach produces significant improvement over the K-means partition clustering technique.

Data clustering is an approach for automatically finding classes, concepts, or groups of patterns. It also aims at representing large datasets by a few number of prototypes or clusters. It brings simplicity in modelling data and plays an important role in the process of knowledge discovery and data mining. Data mining tasks require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This imposes computational requirements on the clustering techniques. Swarm Intelligence (SI) has emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This paper looks into the use of Particle Swarm Optimization for cluster analysis. The effectiveness of Fuzzy C-means clustering provides enhanced performance and maintains more diversity in the swarm and allows the particles to be robust to trace the changing environment. Data structure identifying from the large scale data has become a very important in the data mining problems. Cluster analysis identifies groups of similar data items in large datasets which is one of its recent beneficiaries. The increasing complexity and large amounts of data in the data sets that have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. This paper also proposes two new approaches using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters.

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.

2012

In recent years, clustering is still a popular analysis tool for data statistics. The data structure identifying from the large-scale data has become a very important issue in the data mining problem. In this paper, an improved particle swarm optimization based on Gauss chaotic map for clustering is proposed. Gauss chaotic map adopts a random sequence with a random starting point as a parameter, and relies on this parameter to update the positions and velocities of the particles. It provides the significant chaos distribution to balance the exploration and exploitation capability for search process. This easy and fast function generates a random seed processes, and further improve the performance of PSO due to their unpredictability. In the experiments, the eight different clustering algorithms were extensively compared on six test data. The results indicate that the performance of our proposed method is significantly better than the performance of other algorithms for data clusteri...

This paper presents an efficient hybrid method, namely fuzzy particle swarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzy clustering problem, especially for large sizes. When the problem becomes large, the FCM algorithm may result in uneven distribution of data, making it difficult to find an optimal solution in reasonable amount of time. The PSO algorithm does find a good or near-optimal solution in reasonable time, but its performance was improved by seeding the initial swarm with the result of the c-means algorithm. The fuzzy c-means, PSO and FPSO algorithms are compared using the performance factors of object function value (OFV) and CPU execution time. It was ascertained that the computational times for the FPSO method outperforms the FCM and PSO method and had higher solution quality in terms of the objective function value (OFV).

International Journal of Computer Applications, 2014

Data mining is the collection of different techniques. Clustering information into various cluster is one of the data mining technique. It is a method, in which each cluster must contain more similar data and have much dissimilarity between inter cluster data. Most of traditional clustering algorithms have disadvantages like initial centroid selection, local optima, low convergence rate etc. Clustering with swarm based algorithms is emerging as an alternative to more conventional clustering techniques. In this paper, a new hybrid sequential clustering approach is proposed, which uses PSO -a swarm based technique in sequence with Fuzzy k -means algorithm in data clustering. Experimentation was performed on standard dataset available online. From the result, the proposed approach helps to overcome limitations of both algorithms, improves quality of formed cluster and avoids being trapped in local optima.