Data clustering using particle swarm optimization

Merwe, D.W. van der; Engelbrecht, Andries P.

doi:10.1109/cec.2003.1299577

Cited by 474 publications

(279 citation statements)

References 5 publications

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“…However, few adaptations have been presented for document clustering. Most are combinations of PSO with traditional clustering algorithms as K-Means [8,40,49]. In [8] a global search process is carried out by the PSO algorithm and then the best result obtained by the PSO algorithm is used for determining the initial centroids of the K-Means algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In [8] a global search process is carried out by the PSO algorithm and then the best result obtained by the PSO algorithm is used for determining the initial centroids of the K-Means algorithm. In [40] the authors integrated the simplicity of the K-Means algorithm with the effectiveness of PSO in a unique algorithm while in [49] the result obtained by a K-Means algorithm is used as a single particle in the initial swarm of the PSO algorithm. These combinations of PSO with a clustering algorithm aim to improve the capabilities to obtain good clusters.…”

Section: Related Workmentioning

confidence: 99%

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An efficient Particle Swarm Optimization approach to cluster short texts

Cagnina

Errecalde

Ingaramo

et al. 2014

Information Sciences

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Short texts such as evaluations of commercial products, news, FAQ's and scientific abstracts are important resources on the Web due to the constant requirements of people to use this on line information in real life. In this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this type of problems. CLUDIPSO obtained high quality results with small corpora although, with larger corpora, a significant deterioration of performance was observed. This article presents CLUDIPSO ⋆ , an improved version of CLUDIPSO, which includes a different representation of particles, a more efficient evaluation of the function to be optimized and some modifications in the mutation operator. Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO ⋆ is an effective clustering method for short-text corpora of small and medium size.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An efficient Particle Swarm Optimization approach to cluster short texts

Cagnina

Errecalde

Ingaramo

et al. 2014

Information Sciences

View full text Add to dashboard Cite

show abstract

“…This demands that the algorithm not only find the optimal solution but also track the trajectory of the non-stationary optimal solution. Particle Swarm Optimization (PSO) [1] has been proven to be both effective and efficient in solving a diverse set of optimization problems [2,3]. In the past several years, PSO has been successfully applied in many research and application areas.…”

Section: Introductionmentioning

confidence: 99%

A Simple Distributed Particle Swarm Optimization for Dynamic and Noisy Environments

Cui

Charles

Potok

2009

Nature Inspired Cooperative Strategies for Optimization (NICSO 2008)

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Abstract. In this paper, we present a Simple Distributed Particle Swarm Optimization (SDPSO) algorithm that can be used to track the optimal solution in a dynamic and noisy environment. The classic PSO algorithm lacks the ability to track changing optimum in a dynamic environment. Several approaches have been investigated to enhance the PSO algorithm's ability in dynamic environments. However, in dealing with dynamic environments, these approaches have lost PSO's original strengths of decentralized control and ease of implementation. The SDPSO algorithm proposed in this paper maintains these classic PSO features as well as provides the optimum result tracking capability in dynamic environments. In this research, the DF1 multimodal dynamic environment generator proposed by Morrison and De Jong is used to evaluate the classic PSO, SDPSO and other two adaptive PSOs.

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“…Merwe et al used PSO algorithm to solve k-means clustering problem. The algorithm is extended to use k-means clustering to seed the initial swarm [45].…”

Section: Related Workmentioning

confidence: 99%

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Bouyer

2016

Foundations of Computing and Decision Sciences

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Abstract. Among the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initialization problem of k-means algorithm, but it also has local optima problem. In this paper, we develop a new algorithm for solving this problem based on an improved version of particle swarm optimization (IPSO) algorithm and KHM clustering. In the proposed algorithm, IPSO is equipped with Cuckoo Search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence and escape from local optima. IPSO updates positions of particles based on a combination of global worst, global best with personal worst and personal best to dynamically be used in each iteration of the IPSO. The experimental result on five real-world datasets and two artificial datasets confirms that this improved version is superior to k-harmonic means and regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy and correctness while markedly improving the processing time.

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Data clustering using particle swarm optimization

Cited by 474 publications

References 5 publications

An efficient Particle Swarm Optimization approach to cluster short texts

An efficient Particle Swarm Optimization approach to cluster short texts

A Simple Distributed Particle Swarm Optimization for Dynamic and Noisy Environments

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

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