This article describes an improvement for K-means algorithm and its application in the form of a system that clusters search results retrieved from Wikipedia. The proposed algorithm eliminates K-means disadvantages an...
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In this paper, a new scalable clustering method named "APANC" (Affinity Propagation And Normalized Cut) is proposed. During the APANC process, we firstly use the "Affinity Propagation" (AP) to prel...
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Soft subspace clustering (SSC) methods can simultaneously performance clustering and find the subspace where each cluster lie in. A Minkowski metric based SSC (MSSC) algorithm recently is proposed to improve the adapt...
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In this paper, a novel chaos particle swarm optimization(PSO) clustering algorithm for texture image segmentation was proposed. By means of stochastic property and ergodicity of chaos search mechanism, chaos PSO algor...
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This paper presents a clustering ensemble method based on our novel three-staged clustering algorithm. A clustering ensemble is a paradigm that seeks to best combine the outputs of several clustering algorithms with a...
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Data mining is a critical data analysis technique for extracting hidden information from large databases for business or industrial applications. As the size of organizational databases increase, finding information a...
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This paper presents a combined supervised and unsupervised approach for multidocument person name disambiguation. Based on feature vectors reflecting pairwise comparisons between web pages, a classification algorithm ...
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The clusters tend to have vague or imprecise boundaries in some fields such as web mining, since clustering has been widely used. Fuzzy clustering is sensitive to noises and possibilistic clustering is sensitive to th...
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This paper deals with clustering of speakers’ short segments, in a scenario where additional segments continue to arrive and should be constantly clustered together with previous segments that were already clustered....
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This paper presents a variant of the parallel execution of certain phases of the clustering of documents using the algorithm FRiS-Cluster. We give quantitative values of time the process takes to demonstrate the benef...
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This paper presents a variant of the parallel execution of certain phases of the clustering of documents using the algorithm FRiS-Cluster. We give quantitative values of time the process takes to demonstrate the benefits of implementing the parallel implementation of various stages of processing: A preliminary analysis of documents, which includes calculation of similarity measures, and partly in the performance of the clustering process itself.
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