Results 201 to 210 of about 2,690,463 (245)
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Parallel clustering algorithms
Parallel Computing, 1989zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiaobo Li 0001, Zhixi Fang
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An online clustering algorithm
2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2011This paper presents a new online clustering algorithm called SAFN which is used to learn continuously evolving clusters from non-stationary data. The SAFN uses a fast adaptive learning procedure to take into account variations over time. In non-stationary and multi-class environment, the SAFN learning procedure consists of five main stages: creation ...
Kan Li, Fenglan Yao, Ruipeng Liu
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2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
In view of the fact that DBSCAN clustering algorithm can identify the data with arbitrary shape and one-pass clustering algorithm has the quick and efficient feature, this paper proposes a two-stage hybrid clustering algorithm. DBSCAN is improved to process the data with categorical attributes.
Sheng-Yi Jiang, Xia Li
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In view of the fact that DBSCAN clustering algorithm can identify the data with arbitrary shape and one-pass clustering algorithm has the quick and efficient feature, this paper proposes a two-stage hybrid clustering algorithm. DBSCAN is improved to process the data with categorical attributes.
Sheng-Yi Jiang, Xia Li
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2009 IEEE International Conference on Systems, Man and Cybernetics, 2009
The paper presents a new graph based clustering algorithm. Traditional clustering algorithms have the drawback that it takes large number of iterations in order to come up with the desired number of clusters. The advantage of this approach is that the size of the dataset is reduced using graph based clustering approach and the required number of ...
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The paper presents a new graph based clustering algorithm. Traditional clustering algorithms have the drawback that it takes large number of iterations in order to come up with the desired number of clusters. The advantage of this approach is that the size of the dataset is reduced using graph based clustering approach and the required number of ...
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Computers & Operations Research, 2007
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Kweku-Muata Osei-Bryson, Tasha R. Inniss
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Kweku-Muata Osei-Bryson, Tasha R. Inniss
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Proceedings of the 24th international conference on Machine learning, 2007
By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover. After having introduced this novel approach to unsupervised
Esma Aïmeur +2 more
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By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover. After having introduced this novel approach to unsupervised
Esma Aïmeur +2 more
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Analysis of Clustering Algorithms
2016Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many ...
Iryna Zheliznyak +2 more
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European Journal of Operational Research, 2001
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Yu-Chiun Chiou, Lawrence W. Lan
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Yu-Chiun Chiou, Lawrence W. Lan
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A GRASP Algorithm for Clustering
2002We present a new approach for Cluster Analysis based on a Greedy Randomized Adaptive Search Procedure (GRASP), with the objective of overcoming the convergence to a local solution. It uses a probabilistic greedy Kaufman initialization for getting initial solutions and K-Means algorithm as a local search algorithm.
José Ramón Cano +3 more
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CLUSTER ALGORITHMS FOR SURFACES
International Journal of Modern Physics C, 1992We discuss a new cluster algorithm that completely eliminates critical slowing down for surface models of the SOS (solid-on-solid) type.
Evertz, Hans Gerd +4 more
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