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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Pattern recognition Jg. 42; H. 7; S. 1223 - 1236
Hauptverfasser: De Carvalho, Francisco de A.T., Lechevallier, Yves
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.07.2009
Elsevier
Schlagworte:
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-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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, these methods use single adaptive (city-block and Hausdorff) distances that change at each iteration, but are the same for all clusters. Moreover, various tools for the partition and cluster interpretation of symbolic interval data furnished by these algorithms are also presented. Experiments with real and synthetic symbolic interval data sets demonstrate the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2008.11.016