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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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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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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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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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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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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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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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Clustering Criteria and Algorithms
2009Cluster analysis is an unsupervised pattern recognition frequently used in biology, where large amounts of data must often be classified. Hierarchical agglomerative approaches, the most commonly used techniques in biology, are described in this chapter.
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1991
Magnetic Resonance Imaging (MRI) plays a relevant role in the design of systems for computer assisted diagnosis. MR-images are multi-dimensional in nature; physicians have to combine several perceptual information images to perform the tissue classification needed for diagnosis. Automatic clustering methods help to discriminate relevant features and to
Vito Di Gesù +3 more
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Magnetic Resonance Imaging (MRI) plays a relevant role in the design of systems for computer assisted diagnosis. MR-images are multi-dimensional in nature; physicians have to combine several perceptual information images to perform the tissue classification needed for diagnosis. Automatic clustering methods help to discriminate relevant features and to
Vito Di Gesù +3 more
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