Partitional clustering algorithms for symbolic interval data based on single adaptive distances

This paper introduces dynamic clustering methods for partitioning symbolic interval data. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between clusters and their representatives. To compare symbolic interval data, th...

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Bibliographic Details
Published in:	Pattern recognition Vol. 42; no. 7; pp. 1223 - 1236
Main Authors:	De Carvalho, Francisco de A.T., Lechevallier, Yves
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.07.2009 Elsevier
Subjects:	Adaptive distances Applied sciences Cluster interpretation indices Exact sciences and technology Information, signal and communications theory Partition interpretation indices Partitional clustering methods Signal and communications theory Signal representation. Spectral analysis Signal, noise Symbolic data analysis Symbolic interval data Telecommunications and information theory Symbolic data analysis Symbolic interval data Partition interpretation indices Partitional clustering methods Cluster interpretation indices Adaptive distances Data analysis Automatic classification Prototype Partition method Iterative method Urban area Algorithm Signal classification Adaptive method Dynamic method Optimization Cluster interprétation indices
ISSN:	0031-3203, 1873-5142
Online Access:	Get full text
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