An approach for document clustering using PSO and K-means algorithm

Chouhan, Rashmi; Purohit, Anuradha

doi:10.1109/icisc.2018.8399034

Cited by 18 publications

(7 citation statements)

References 5 publications

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“…Since the clustering is unsupervised, the optimal K value cannot be determined based on the classification results. There have been studies on the optimal K value [33,34]. In this study, the contour coefficient method was selected to determine the K value of the clustering of the solid wood panel images.…”

Section: Clustering Based On Color Featuresmentioning

confidence: 99%

Color Classification and Texture Recognition System of Solid Wood Panels

Wang

Zhuang

Liu

et al. 2021

Forests

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Solid wood panels are widely used in the wood flooring and furniture industries, and paneling is an excellent material for indoor decoration. The classification of colors helps to improve the appearance of wood products assembled from multiple panels due to the differences in surface colors of solid wood panels. Traditional wood surface color classification mainly depends on workers’ visual observations, and manual color classification is prone to visual fatigue and quality instability. In order to reduce labor costs of sorting and to improve production efficiency, in this study, we introduced machine vision technology and an unsupervised learning technique. First-order color moments, second-order color moments, and color histogram peaks were selected to extract feature vectors and to realize data dimension reduction. The feature vector set was divided into different clusters by the K-means algorithm to achieve color classification and, thus, the solid wood panels with similar surface color were classified into one category. Furthermore, during twice clustering based on second-order color moment, texture recognition was realized on the basis of color classification. A sample of beech wood was selected as the research object, not only was color classification completed, but texture recognition was also realized. The experimental results verified the effectiveness of the technical proposal.

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Section: Clustering Based On Color Featuresmentioning

confidence: 99%

Color Classification and Texture Recognition System of Solid Wood Panels

Wang

Zhuang

Liu

et al. 2021

Forests

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“…In [46], low-energy clustering problem approach for low-energy clustering problems in WSNs to minimize the communication energy consumption is researched based on PSO. In their article, they minimize the communication energy consumption without considering energy restriction.…”

Section: Related Workmentioning

confidence: 99%

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Yang

Zhou

2021

Scientific Programming

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Because the sensors are constrained in energy capabilities, low-energy clustering has become a challenging problem in high-density wireless sensor networks (HDWSNs). Usually, sensor nodes tend to be tiny devices along with constrained clustering abilities. To have a low communication energy consumption, a low-energy clustering scheme should be designed properly. In this work, a new cloned chaotic parallel evolution algorithm (CCPEA) is proposed, and a low-energy clustering model is established to lower the communication energy consumption of HDWSNs. By introducing CCPEA into the low-energy clustering, an objective function is designed for evaluating the communication energy consumption. For this problem, we define a clone operator to minimize the communication energy consumption of HDWSNs, use the chaotic operator to randomly generate the initial population to expand the search range to avoid local optimization, and find the parallel operator to speed up the convergence speed. In the experiment, the effect of CCPEA is compared to heuristic approaches of particle swarm optimization (PSO) and simulated annealing (SA) for the HDWSNs with different numbers of sensors. Simulation experiments demonstrate that the presented CCPEA method achieves a lower communication energy consumption and faster convergence speed than PSO and SA.

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“…In view of the shortcomings of PSO when applied to large data sets, particles on the boundary of the search space cannot be moved to a better position, and a mapping method is proposed. Reference [37] proposed an approach for document clustering using the particle swarm optimization method. This method is applied before K-means for finding optimal points in the search space, and these points are used as initial cluster centroids for the K-means algorithm to find the final clusters of documents.…”

Section: Related Workmentioning

confidence: 99%

A Novel Clustering Algorithm Based on DPC and PSO

et al. 2020

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Analyzing the fast search and find of density peaks clustering (DPC) algorithm, we find that the cluster centers cannot be determined automatically and that the selected cluster centers may fall into a local optimum and the random selection of the parameter cutoff distance d c value. To overcome these problems, a novel clustering algorithm based on DPC & PSO (PDPC) is proposed. Particle swarm optimization (PSO) is introduced because of its simple concept and strong global search ability, which can find the optimal solution in relatively few iterations. First, to solve the effect of the selection of the parameter d c on the calculation density and the clustering results, this paper proposes a method to calculate that parameter. Second, a new fitness criterion function is proposed that iteratively searches K global optimal solutions through the PSO algorithm, that is, the initial cluster centers. Third, each sample is assigned to K initial center points according to the minimum distance principle. Finally, we update the cluster centers and redistribute the remaining objects to the clusters closest to the cluster centers. Furthermore, the effectiveness of the proposed algorithm is verified on nine typical benchmark data sets. The experimental results show that the PDPC can effectively solve the problem of cluster center selection in the DPC algorithm, avoiding the subjectivity of the manual selection process and overcoming the influence of the parameter d c. Compared with the other six algorithms, the PDPC algorithm has a stronger global search ability, higher stability and a better clustering effect. INDEX TERMS Clustering, density peak, particle swarm optimization, fitness function.

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An approach for document clustering using PSO and K-means algorithm

Cited by 18 publications

References 5 publications

Color Classification and Texture Recognition System of Solid Wood Panels

Color Classification and Texture Recognition System of Solid Wood Panels

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

A Novel Clustering Algorithm Based on DPC and PSO

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