Enhanced bisecting -means clustering using intermediate cooperation

Kashef, Rasha; Kamel, Mohamed S.

doi:10.1016/j.patcog.2009.03.011

Cited by 57 publications

(30 citation statements)

References 22 publications

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“…The top down approach starts by organizing all items in a single cluster and later divides it into smaller groups. Such an operation can be seen in the Bisect Kmeans (Kashef and Kamel, 2009). On the other hand, the bottom up method starts from single clusters that contain a single item.…”

Section: Introductionmentioning

confidence: 94%

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: Introductionmentioning

confidence: 94%

Document Clustering Based on Firefly Algorithm

Mohammed¹,

Yusof²,

Husni³

2015

Journal of Computer Science

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“…Furthermore, if the number of channels is different than 2, then the same algorithm can be iteratively repeated to find the solution, as explained in [3]. For example, the bisecting k -means algorithm [20] and its variants like those in [21] are such algorithms that determine the cluster to be further bisected in the next step according to various criteria defined.…”

Section: System Modeling For Channel Allocation In Wireless Systemsmentioning

confidence: 99%

Continuous-time Hopfield neural network-based optimized solution to 2-channel allocation problem

Uykan

2015

Turk J Elec Eng & Comp Sci

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Abstract:The channel allocation problem in cellular radio systems is NP-complete, and thus its general solution is not known for even the 2-channel case. It is well known that the link gain system matrix (or received-signal power system matrix) of the radio network is (and may be highly) asymmetric, and that as the Hopfield neural network is applied to optimization problems, its weight matrix should be symmetric. The main contribution of this paper is as follows: turning the channel allocation problem into a maxCut graph partitioning problem, we propose a simple and effective continuous-time Hopfield neural network-based solution by determining its symmetric weight matrix from the asymmetric received-signal-power-system matrix. Computer simulations confirm the effectiveness and superiority of the proposed solution as compared to standard algorithms for various illustrative cellular radio scenarios for the 2-channel case.

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“…The better performance of bisecting K-means is because of production of relatively uniform size clusters. In [15] R. Kashef et al presented an enhanced version of BKM, Cooperative Bisecting K-means (CBKM) clustering algorithm, which concurrently combines the results of the BKM and KM at each level of the binary hierarchical tree using cooperative and merging matrices. Experimental results show that the CBKM attains better clustering quality than KM and BKM.…”

Section: Partitioning Clustering Techniquesmentioning

confidence: 99%

Document Clustering: A Review

Bisht¹,

Paul²

2013

IJCA

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ABSTRACT:As the internet is exploding with huge volume of text documents, the need of grouping similar documents together for versatile applications have hold the attention of researchers in this area. Document clustering can facilitate the tasks of document organization and web browsing, search engine results, corpus summarization, documents classification, information retrieval and filtering. However several attempts have been made to develop efficient document clustering algorithms but most of the clustering methods suffer from challenges in dealing with problems of high dimensionality, scalability, accuracy and meaningful cluster labels. This paper intends to provide a brief summary over methods studied and current state of documents clustering research, including basic traditional methods as well as advanced fuzzy based, GA, PSO, HS oriented techniques etc. Also document representation model and its challenges, dimensionality reduction mechanisms, issues in document clustering, and cluster quality evaluation criteria are discussed.

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Enhanced bisecting -means clustering using intermediate cooperation

Cited by 57 publications

References 22 publications

Document Clustering Based on Firefly Algorithm

Document Clustering Based on Firefly Algorithm

Continuous-time Hopfield neural network-based optimized solution to 2-channel allocation problem

Document Clustering: A Review

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