K-means Optimization Clustering Algorithm Based on Particle Swarm Optimization and Multiclass Merging

Lin, Yuan; Nan, Tong; Shi, Majie; Fan, Kedi; Yuan, Di; Qu, Lincong; Fu, Qiang

doi:10.1007/978-3-642-30126-1_90

Cited by 22 publications

(13 citation statements)

References 6 publications

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“…However, on medium-to-large size datasets, which frequently occurs in the data mining field, the effectiveness and efficiency of PSO-KM-based clustering algorithms should be further analyzed, which is one focus of the paper. Similar studies can be found in the literature (e.g., [5][6][7][8][9]). Chen and Ye [10] directly used PSO to solve the K-Means-type clustering problems, with lower clustering performance than most well-designed hybridization strategies.…”

Section: Literature Overviewmentioning

confidence: 54%

A Population-Based Clustering Technique Using Particle Swarm Optimization and K-Means

Niu

Duan

Tan

et al. 2015

Advances in Swarm and Computational Intelligence

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Abstract. Population-based clustering techniques, which attempt to integrate particle swarm optimizers (PSOs) with K-Means, have been proposed in the literature. However, the performance of these hybrid clustering methods have not been extensively analyzed and compared with other competitive clustering algorithms. In the paper, five existing PSOs, which have shown promising performance for continuous function optimization, are hybridized separately with K-Means, leading to five PSO-KM-based clustering methods. Numeric experiments on nine real-life datasets show that, in the context of numeric data clustering, there exist no significant performance differences among these PSOs, though they often show significantly different search abilities in the context of numeric function optimization. These PSO-KM-based clustering techniques obtain better and more stable solutions than some individual-based counterparts, but at the cost of higher time complexity. To alleviate the above issue, some potential improvements are empirically discussed.

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Section: Literature Overviewmentioning

confidence: 54%

A Population-Based Clustering Technique Using Particle Swarm Optimization and K-Means

Niu

Duan

Tan

et al. 2015

Advances in Swarm and Computational Intelligence

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“…Over a time span of 50 years, K-means has been and is still widely used [Jain 2010] and is mainly applied to exploit the K centroids for clustering problem [Lam et al 2012]. Specifically, this algorithm may include the following realization steps [Lin et al 2012]:…”

Section: Applications Of Pso + K-meansmentioning

confidence: 99%

A Network Behavior-Based Botnet Detection Mechanism Using PSO and K-means

Kao

Zhang

et al. 2015

ACM Trans. Manage. Inf. Syst.

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In today's world, Botnet has become one of the greatest threats to network security. Network attackers, or Botmasters, use Botnet to launch the Distributed Denial of Service (DDoS) to paralyze large-scale websites or steal confidential data from infected computers. They also employ "phishing" attacks to steal sensitive information (such as users' accounts and passwords), send bulk email advertising, and/or conduct click fraud. Even though detection technology has been much improved and some solutions to Internet security have been proposed and improved, the threat of Botnet still exists. Most of the past studies dealing with this issue used either packet contents or traffic flow characteristics to identify the invasion of Botnet. However, there still exist many problems in the areas of packet encryption and data privacy, simply because Botnet can easily change the packet contents and flow characteristics to circumvent the Intrusion Detection System (IDS). This study combines Particle Swarm Optimization (PSO) and K-means algorithms to provide a solution to remedy those problems and develop, step by step, a mechanism for Botnet detection. First, three important network behaviors are identified: long active communication behavior (ActBehavior), connection failure behavior (FailBehavior), and network scanning behavior (ScanBehavior). These behaviors are defined according to the relevant prior studies and used to analyze the communication activities among the infected computers. Second, the features of network behaviors are extracted from the flow traces in the network layer and transport layer of the network equipment. Third, PSO and K-means techniques are used to uncover the host members of Botnet in the organizational network. This study mainly utilizes the flow traces of a campus network as an experiment. The experimental findings show that this proposed approach can be employed to detect the suspicious Botnet members earlier than the detection application systems. In addition, this proposed approach is easy to implement and can be further used and extended in the campus dormitory network, home networks, and the mobile 3G network. . 2015. A network behavior-based botnet detection mechanism using PSO and K-means.

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“…Xiao et al [Xiao (2003)] hybridized PSO with Self-Organizing Maps (SOM) to use SOM to cluster the data and PSO to optimize weights of the SOM. Lin et al [Lin, Tong, Shi et al (2014)] used the results of K-means in combination with PSO and multiclass merging to perform data clustering. Omran et al [Omran, Salman and Engelbrecht (2006)] suggested a dynamic clustering algorithm based on PSO and K-means for image segmentation.…”

Section: Introductionmentioning

confidence: 99%

An Improved Unsupervised Image Segmentation Method Based on Multi-objective Particle Swarm Optimization Clustering Algorithm

Liu¹,

Bao²,

Song³

et al. 2019

Computers, Materials &Amp; Continua

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Most image segmentation methods based on clustering algorithms use singleobjective function to implement image segmentation. To avoid the defect, this paper proposes a new image segmentation method based on a multi-objective particle swarm optimization (PSO) clustering algorithm. This unsupervised algorithm not only offers a new similarity computing approach based on electromagnetic forces, but also obtains the proper number of clusters which is determined by scale-space theory. It is experimentally demonstrated that the applicability and effectiveness of the proposed multi-objective PSO clustering algorithm

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K-means Optimization Clustering Algorithm Based on Particle Swarm Optimization and Multiclass Merging

Cited by 22 publications

References 6 publications

A Population-Based Clustering Technique Using Particle Swarm Optimization and K-Means

A Population-Based Clustering Technique Using Particle Swarm Optimization and K-Means

A Network Behavior-Based Botnet Detection Mechanism Using PSO and K-means

An Improved Unsupervised Image Segmentation Method Based on Multi-objective Particle Swarm Optimization Clustering Algorithm

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