Results 261 to 270 of about 25,970,551 (304)

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
openaire   +2 more sources

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
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source

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.
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

Clustering mixed data

WIREs Data Mining and Knowledge Discovery, 2011
AbstractMixture model clustering proceeds by fitting a finite mixture of multivariate distributions to data, the fitted mixture density then being used to allocate the data to one of the components. Common model formulations assume that either all the attributes are continuous or all the attributes are categorical.
Lynette A. Hunt, Murray A. Jorgensen
openaire   +2 more sources

Clustering for Data Matching

2006
The problem of matching data has as one of its major bottlenecks the rapid deterioration in performance of time and accuracy, as the amount of data to be processed increases. One reason for this deterioration in performance is the cost incurred by data matching systems when comparing data records to determine their similarity (or dissimilarity ...
Edward Tersoo Apeh, Bogdan Gabrys
openaire   +1 more source