Particle swarm optimization algorithm and its application to clustering analysis

Chen, Ching-Yi; Ye, Fun

doi:10.1109/icnsc.2004.1297047

Cited by 91 publications

(12 citation statements)

References 8 publications

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“…PSO has shown superior performance in solving optimization problem [16]. Recently, this method is also applied for data clustering [17], [18].…”

Section: B Particle Swarm Optimizationmentioning

confidence: 99%

Automatic Clustering Using an Improved Particle Swarm Optimization

Kuo¹,

Zulvia²

2013

JIII

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Unsupervised data clustering is an important analysis in data mining. Many clustering algorithms have been proposed, yet most of them require predefined number of clusters. Unfortunately, unavailable information regarding number of clusters is commonly happened in real-world problems. Thus, this paper intends to overcome this problem by proposing an algorithm for automatic clustering. The proposed algorithm is developed based on a population-based heuristic method named particle swarm optimization (PSO). It overcomes two main issues in automatic clustering, namely determining number of clusters and cluster centroid. In the automatic clustering using PSO (ACPSO), the exploration is conducted by particles comprising of two sections. Herein, time-varying tuning parameter is applied. Furthermore, sigmoid function is employed to handle infeasible solution. In addition, K-means is applied to adjust the cluster centroids. Method validation using four benchmark datasets reveals that TPSO outperforms other two previous methods namely DCPSO, DCPG, and DCGA. Overall, ACPSO has better accuracy and consistency. 

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“…PSO has shown superior performance in solving optimization problem [16]. Recently, this method is also applied for data clustering [17], [18].…”

Section: B Particle Swarm Optimizationmentioning

confidence: 99%

Automatic Clustering Using an Improved Particle Swarm Optimization

Kuo¹,

Zulvia²

2013

JIII

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“…Particle swarm optimization (PSO) (Eberhart and Kennedy 1995; Kennedy and Eberhart 1995;Fourie and Groenwold 2002) has been applied to clustering of feature vectors by several people (Ching-Yi and Fun 2004;Feng et al 2006;Zhang et al 2006). A related method, genetic algorithms, has also been used in clustering by Hall et al (1994Hall et al ( , 1999, Maulik and Bandyopadhyay (2000), Babu and Murti (1994) and Bhuyan et al (1991).…”

Section: Introductionmentioning

confidence: 98%

Fuzzy relational clustering based on comparing two proximity matrices with utilization of particle swarm optimization

Brouwer

Groenwold

2008

Soft Comput

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“…A well-known type of partitional clustering algorithms is the centre-based clustering method, and the most popular and widely used algorithm from this class of algorithms is known as K-means algorithm. Kmeans is relatively easy to implement and effective most of the time [7,13]. However, the performance of k-means depends on the initial state of centroids which is likely to converge to the local optima rather than global optima.…”

Section: Introductionmentioning

confidence: 98%

A Hybridized Approach for Prioritizing Software Requirements Based on K-Means and Evolutionary Algorithms

Achimugu

Selamat

2014

Studies in Computational Intelligence

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One of the major challenges facing requirements prioritization techniques is accuracy. The issue here is lack of robust algorithms capable of avoiding a mismatch between ranked requirements and stakeholder's linguistic ratings. This problem has led many software developers in building systems that eventually fall short of user's requirements. In this chapter, we propose a new approach for prioritizing software requirements that reflect high correlations between the prioritized requirements and stakeholders' linguistic valuations. Specifically, we develop a hybridized algorithm which uses preference weights of requirements obtained from the stakeholder's linguistic ratings. Our approach was validated with a dataset known as RALIC which comprises of requirements with relative weights of stakeholders.

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Particle swarm optimization algorithm and its application to clustering analysis

Cited by 91 publications

References 8 publications

Automatic Clustering Using an Improved Particle Swarm Optimization

Automatic Clustering Using an Improved Particle Swarm Optimization

Fuzzy relational clustering based on comparing two proximity matrices with utilization of particle swarm optimization

A Hybridized Approach for Prioritizing Software Requirements Based on K-Means and Evolutionary Algorithms

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