Document clustering using particle swarm optimization

Cui, Xiaohui; Potok, Thomas E.; Palathingal, Paul J.

doi:10.1109/sis.2005.1501621

Cited by 248 publications

(133 citation statements)

References 11 publications

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“…Nevertheless, K-means always converge into local optima. Such a situation has led researchers to the K-means with Swarm intelligent algorithms in order to search for optimal solution (Cui et al, 2005;He et al, 2006;Karaboga and Ozturk, 2011;Zaw and Mon, 2013). The pseudo code of K-means (Jain, 2010) shows in Fig.…”

Section: Related Workmentioning

confidence: 99%

“…Metaheuristic optimization algorithm is utilized to find optimal or near optimal solution. It is proven successful in many hard problems such as speech recognition (Hassanzadeh et al, 2012), image processing (Horng and Jiang, 2010) and text clustering (Cui et al, 2005;He et al, 2006;Karaboga and Ozturk, 2011;Zaw and Mon, 2013). Meta-heuristic optimization algorithm includes two important components; exploration and exploitation.…”

Section: Related Workmentioning

confidence: 99%

“…Swarm Intelligent is defined as "The emergent collective intelligence of groups of simple agents" (Bonabeau et al, 1999). Examples of Swarm Intelligent algorithms includes the Artificial Bee Colony (ABC) (Karaboga and Ozturk, 2011), Cuckoo Search Optimization algorithm (Zaw and Mon, 2013), Ant Colony Optimization (He et al, 2006) and particle swarm optimization (Cui et al, 2005). These types of Swarm Intelligent algorithms have been utilized in text clustering; however, they need to predefine the number of k clusters.…”

Section: Introductionmentioning

confidence: 99%

“…A benchmark dataset obtained from 20Newsgroups is used to demonstrate the effectiveness of the proposed FA methods. Comparison of performance is made against the Particle Swarm Optimization (PSO) (Cui et al, 2005), K-means (Jain, 2010) and a hybrid model of FA and K-means (Rui et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Document Clustering Based on Firefly Algorithm

Mohammed¹,

Yusof²,

Husni³

2015

Journal of Computer Science

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Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters. Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization. This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering. We present two variants of FA; Weight-based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFA II ). The difference between the two algorithms is that the WFA II, includes a more restricted condition in determining members of a cluster. The proposed FA methods are later evaluated using the 20Newsgroups dataset. Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. The obtained results demonstrated that the WFA II outperformed the WFA, PSO, K-means and FA-Kmeans. This result indicates that a better clustering can be obtained once the exploitation of a search solution is improved.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Document Clustering Based on Firefly Algorithm

Mohammed¹,

Yusof²,

Husni³

2015

Journal of Computer Science

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“…It is capable of solving the global optimal solution of non-linear and non-differentiable problem and has been received considerable attentions [37][38][39]. The PSO is initialized with a population of random particles, and each particle is treated as a solution with two states: the current position x i = (x i1 , x i2 , .…”

Section: Parameter Estimation Algorithm Based On Psomentioning

confidence: 99%

Cfar Target Detection in Ground Sar Image Based on Kk Distribution

Gao¹,

Zhan²,

Wan³

et al. 2013

PIER

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Abstract-This paper deals with the problem of constant false alarm rate (CFAR) target detection in high-resolution ground synthetic aperture radar (SAR) images based on KK distribution.For the parameter estimation of KK distribution, the semi-experiential algorithm is analyzed firstly. Then a new estimation algorithm based on the particle swarm optimization (PSO) is proposed, which takes the discrepancies between the histogram of the clutter data and probability density function (PDF) of KK distribution at some selected points as the cost function to search for the optimal parameter values using PSO algorithm. The performance of the two algorithms is compared using Monte-Carlo simulation using the simulated data sets generated under different conditions; and the estimation results validate the better performance of the new algorithm. Then the KK distribution, which is proposed for spiky sea clutter originally, is applied to model the real ground SAR clutter data. The goodness-of-fit test clearly show that the KK distribution is able to model the ground SAR clutter much better than some common used model, such as standard Kdistribution and Gamma, etc. On this basis, a global CFAR target detection algorithm is presented. The detection threshold is calculated numerically through the cumulative density function (CDF) of KK distribution. Comparing the amplitude of every SAR image pixel with this threshold, the potential targets in ground SAR images can be located effectively. Then target clustering is implemented to eliminate the false alarm and obtain more accurate target regions. The detection results of the proposed algorithm in a typical ground SAR image show that it has better performance than the detector based on G 0 distribution.

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Wasp swarm optimization of the c-means clustering model

Runkler

2008

Int. J. Intell. Syst.

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This paper deals with clustering by optimizing the c-means clustering model. For some data sets this clustering model possesses many local optima, so conventional alternating optimization (AO) will produce bad results. For obtaining good clustering results, the minimization procedure has to be kept from being trapped in these local optima, for example, by stochastic optimization approaches. Recently, we showed that ant colony optimization (ACO) can be effectively applied to the c-means clustering model. In this paper, we introduce a wasp swarm optimization (WSO) algorithm to optimize the c-means clustering model. In experiments with four benchmark data sets, the new WSO clustering algorithm is compared with AO and ACO. For data sets leading to c-means models without local optima, both WSO and AO perform better and faster than ACO. For data sets leading to multiple local optima, WSO clearly outperforms both AO and ACO. C

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Document clustering using particle swarm optimization

Cited by 248 publications

References 11 publications

Document Clustering Based on Firefly Algorithm

Document Clustering Based on Firefly Algorithm

Cfar Target Detection in Ground Sar Image Based on Kk Distribution

Wasp swarm optimization of the c-means clustering model

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