Results 221 to 230 of about 796,452 (265)

Clustering Functional Data

Journal of Classification, 2003
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Tarpey, Thaddeus, Kinateder, Kimberly
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Superparamagnetic Clustering of Data

Physical Review Letters, 1996
We present a new approach for clustering, based on the physical properties of an inhomogeneous ferromagnetic model. We do not assume any structure of the underlying distribution of the data. A Potts spin is assigned to each data point and short range interactions between neighboring points are introduced.
, Blatt, , Wiseman, , Domany
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Sequential Data Clustering

2010 Ninth International Conference on Machine Learning and Applications, 2010
An algorithm is presented for clustering sequential data in which each unit is a collection of vectors. An example of such a type of data is speaker data in a speaker clustering problem. The algorithm first constructs affinity matrices between each pair of units, using a modified version of the Point Distribution algorithm which is initially developed ...
Jianfei Wu, Loai Al Nimer, Omar Al Azzam, Charith Chitraranjan, Saeed Salem, Anne M. Denton   +5 more
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Clustering Categorical Data

Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073), 2005
In this paper we propose two methods to study the problem of clustering categorical data. The first method is based on dynamical system approach. The second method is based on the graph partitioning approach.
Zhang Yi 0001, Ada Wai-Chee Fu, Chun Hing Cai, Pheng-Ann Heng   +3 more
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Clustering data streams

Proceedings 41st Annual Symposium on Foundations of Computer Science, 2002
We study clustering under the data stream model of computation where: given a sequence of points, the objective is to maintain a consistently good clustering of the sequence observed so far, using a small amount of memory and time. The data stream model is relevant to new classes of applications involving massive data sets, such as Web click stream ...
Sudipto Guha, Nina Mishra, Rajeev Motwani 0001, Liadan O'Callaghan   +3 more
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Clustering Criteria in Multiobjective Data Clustering

2012
We consider the choice of clustering criteria for use in multiobjective data clustering. We evaluate four different pairs of criteria, three employed in recent evolutionary algorithms for multiobjective clustering, and one from Delattre and Hansen's seminal exact bicriterion method.
Julia Handl, Joshua D. Knowles
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Robust data clustering

2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 2003
We address the problem of robust clustering by combining data partitions (forming a clustering ensemble) produced by multiple clusterings. We formulate robust clustering under an information-theoretical framework; mutual information is the underlying concept used in the definition of quantitative measures of agreement or consistency between data ...
Ana L. N. Fred, Anil K. Jain 0001
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Data Mining: Clustering

2019
This article presents a broad overview of the main clustering methodologies. It is accomplished by introducing the clustering problem and the key elements characterizing it. In particular, we describe different distance and similarity measures which can be used in a clustering method.
Amelio A., Tagarelli A.
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Clustering by data competition

Science China Information Sciences, 2012
Clustering analysis is an unsupervised method to find out hidden structures in datasets. Most partitional clustering algorithms are sensitive to the selection of initial exemplars, the outliers and noise. In this paper, a novel technique called data competition algorithm is proposed to solve the problems. First the concept of aggregation field model is
Zhimao Lu, Qi Zhang
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Multiobjective data clustering

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004
Conventional clustering algorithms utilize a single criterion that may not conform to the diverse shapes of the underlying clusters. We offer a new clustering approach that uses multiple clustering objective functions simultaneously. The proposed multiobjective clustering is a two-step process.
Martin H. C. Law, Alexander P. Topchy, Anil K. Jain 0001   +2 more
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