New clustering methods for interval data

New clustering methods for interval data

In this paper we propose two clustering methods for interval data based on the dynamic cluster algorithm. These methods use different homogeneity criteria as well as different kinds of cluster representations (prototypes). Some tools to interpret the final partitions are also introduced. An applicat...

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Bibliographic Details
Published in:Computational statistics Vol. 21; no. 2; pp. 211 - 229
Main Authors: Chavent, Marie, de Carvalho, Francisco de A. T., Lechevallier, Yves, Verde, Rosanna
Format: Journal Article
Language:English
Published: Heidelberg Springer Nature B.V 01-01-2006
Springer Verlag
Subjects:
Algorithms
Clustering
Clusters
Computer Science
Criteria
Homogeneity
Intervals
Machine Learning
Operations research
Other Statistics
Representations
Statistics
Studies
distances
Dynamic clustering
prototypes
interval data
Online Access:Get full text
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Summary:In this paper we propose two clustering methods for interval data based on the dynamic cluster algorithm. These methods use different homogeneity criteria as well as different kinds of cluster representations (prototypes). Some tools to interpret the final partitions are also introduced. An application of one of the methods concludes the paper.[PUBLICATION ABSTRACT]
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-006-0260-0