A Weighted Deterministic Annealing Algorithm for Data Clustering

Yang, Xulei; Song, Qing Song; Cao, Aize

doi:10.5019/j.ijcir.2006.47

Cited by 2 publications

(1 citation statement)

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“…In order to promote the algorithm to search the solution space at the initial stage and accelerate the algorithm to converge to the optimal result at the later stage, we adopt the variable parameter strategy for seeking memory SM N , seeking range SR P , changed dimension CD N , and the mixture ratio mr R defined as Eq. (8).…”

Section: Resultsmentioning

confidence: 99%

Data Clustering with Cat Swarm Optimization

Liu¹,

Shen²

2010

JCIT

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In this article, a recent metaheuristic method, cat swarm optimization, is introduced to find the proper clustering of data sets. Two clustering approaches based on cat swarm optimization called Cat Swarm Optimization Clustering (CSOC) and K-harmonic means Cat Swarm Optimization Clustering (KCSOC) are proposed. In the proposed methods, seeking mode and tracing mode are adopted to exploit and explore the solution space. In addition, K-Harmonic Means (KHM) operation is designed to refine the population and accelerate the convergence of the clustering algorithm. Experimental results on six real life data sets are given to illustrate the effectiveness of the proposed algorithms.

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Section: Resultsmentioning

confidence: 99%

Data Clustering with Cat Swarm Optimization

Liu¹,

Shen²

2010

JCIT

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Medoid Determination in Deterministic Annealing-based Pairwise Clustering

Lee¹,

Lee²

2011

International Journal of Fuzzy Logic and Intelligent Systems

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The deterministic annealing-based clustering algorithm is an EM-based algorithm which behaves like simulated annealing method, yet less sensitive to the initialization of parameters. Pairwise clustering is a kind of clustering technique to perform clustering with inter-entity distance information but not enforcing to have detailed attribute information. The pairwise deterministic annealing-based clustering algorithm repeatedly alternates the steps of estimation of mean-fields and the update of membership degrees of data objects to clusters until termination condition holds. Lacking of attribute value information, pairwise clustering algorithms do not explicitly determine the centroids or medoids of clusters in the course of clustering process or at the end of the process. This paper proposes a method to identify the medoids as the centers of formed clusters for the pairwise deterministic annealing-based clustering algorithm. Experimental results show that the proposed method locate meaningful medoids.clustering, data analysis, deterministic annealing, pairwise clustering

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A Weighted Deterministic Annealing Algorithm for Data Clustering

Cited by 2 publications

References 0 publications

Data Clustering with Cat Swarm Optimization

Data Clustering with Cat Swarm Optimization

Medoid Determination in Deterministic Annealing-based Pairwise Clustering

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