A novel ant-based clustering algorithm using the kernel method

Zhang, Lei; Cao, Qixin

doi:10.1016/j.ins.2010.11.005

Cited by 47 publications

(42 citation statements)

References 36 publications

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“…In order to overcome the parameter setting problem of HS, which is very tedious and could be another daunting optimization problem, Geem et al [53] introduced a variant of HS which eliminates tedious and difficult parameter assigning efforts.…”

Section: Check Stopping Criterionmentioning

confidence: 99%

“…Specifically, optimization based methods define a global objective function over the quality of clustering algorithm and traverse the search space trying to optimize its value. Any general purpose optimization method can serve as the basis of this approach such as Genetic Algorithms (GAs) [10,26,40], Ant Colony Optimization [43,46] and Particle Swarm Optimization [11,12,53], which have been used for web page and image clustering. Since stochastic optimization approaches are good at avoiding convergence to a locally optimal solution, these approaches could be used to find a global nearoptimal solution [35,30,48].…”

Section: Introductionmentioning

confidence: 99%

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Efficient stochastic algorithms for document clustering

Forsati

Mahdavi

Shamsfard

et al. 2013

Information Sciences

111

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Clustering has become an increasingly important and highly complicated research area for targeting useful and relevant information in modern application domains such as the World Wide Web. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm may generate a local optimal clustering. In this paper, we present novel document clustering algorithms based on the Harmony Search (HS) optimization method. By modeling clustering as an optimization problem, we first propose a pure HS based clustering algorithm that finds near-optimal clusters within a reasonable time. Then, harmony clustering is integrated with the K-means algorithm in three ways to achieve better clustering by combining the explorative power of HS with the refining power of the K-means. Contrary to the localized searching property of K-means algorithm, the proposed algorithms perform a globalized search in the entire solution space. Additionally, the proposed algorithms improve K-means by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, therefore, making it more stable. The behavior of the proposed algorithm is theoretically analyzed by modeling its population variance as a Markov chain. We also conduct an empirical study to determine the impacts of various parameters on the quality of clusters and convergence behavior of the algorithms. In the experiments, we apply the proposed algorithms along with K-means and a Genetic Algorithm (GA) based clustering algorithm on five different document datasets. Experimental results reveal that the proposed algorithms can find better clusters and the quality of clusters is comparable based on F-measure, Entropy, Purity, and Average Distance of Documents to the Cluster Centroid (ADDC).

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Section: Check Stopping Criterionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient stochastic algorithms for document clustering

Forsati

Mahdavi

Shamsfard

et al. 2013

Information Sciences

111

View full text Add to dashboard Cite

show abstract

“…To improve the algorithm's efficiency and clustering quality, we incorporated the kernel method into ant-based clustering and created the novel ant-based clustering with the kernel method (ACK) [27]. The applications of kernels in ACK are shown in two ways.…”

Section: Ant-based Clustering Algorithm With the Kernel Methodmentioning

confidence: 99%

“…On the other hand, the algorithm should have the ability to cluster the datasets with different structures. We have proposed a novel ant-based clustering algorithm using the kernel method to overcome these problems [27]. In this paper, we will show how this algorithm is applied in performance assessment for a fleet of machines.…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment for a Fleet of Machines Using a Combined Method of Ant-Based Clustering and CMAC

Zhang

Cao

Lee

2013

Advances in Mechanical Engineering

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This paper proposes a combined method of ant-based clustering and cerebellar model articulation controller for performance assessment for a fleet of machines. A novel ant-based clustering algorithm with kernel method is used to cluster machines in a fleet. The algorithm has two features. First, a projection based on kernel principal component analysis replaces random projection to improve the efficiency. Second, the clustering is performed on the feature space after kernel mapping to improve the clustering accuracy. The algorithm can cluster machines in a self-organizing way to achieve the horizontal assessment. The vertical assessment for the single machine based on CMAC is presented. Then, how to combine the vertical and horizontal assessment results is discussed. The outlier mining method to detect abnormal machines based on the clustering results is also proposed. Cluster-based global outlying factor is suggested to measure the outlying degree of abnormal machine. Finally, the case study on axial fans shows that the combined method can give a more comprehensive assessment for fans' performance monitoring.

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“…Note that there has been a fair amount of interest in recent years in movement on toroidal grids (see for instance [6,9,15]). For discussion of connections between Cayley graphs and networks, in general, see for instance [10,16] and the references therein.…”

Section: Preliminaries and Notationmentioning

confidence: 99%