Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances
This paper presents partitioning dynamic clustering methods for interval-valued data based on suitable adaptive quadratic distances. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their represe...
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| Published in: | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 39; no. 6; pp. 1295 - 1306 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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IEEE
01-11-2009
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| Abstract | This paper presents partitioning dynamic clustering methods for interval-valued data based on suitable adaptive quadratic distances. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These adaptive quadratic distances change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Moreover, various tools for the partition and cluster interpretation of interval-valued data are also presented. Experiments with real and synthetic interval-valued data sets show the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools. |
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| AbstractList | This paper presents partitioning dynamic clustering methods for interval-valued data based on suitable adaptive quadratic distances. These methods furnish a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. These adaptive quadratic distances change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another. Moreover, various tools for the partition and cluster interpretation of interval-valued data are also presented. Experiments with real and synthetic interval-valued data sets show the usefulness of these adaptive clustering methods and the merit of the partition and cluster interpretation tools. |
| Author | Lechevallier, Y. de A.T. de Carvalho, F. |
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| References | ref13 spaeth (ref3) 1980 gordon (ref2) 1999 ref15 ref14 diday (ref8) 1976 diday (ref12) 2008 ref10 ref1 ref17 ref16 ref19 ref18 de souza (ref28) 2004; 3316 diday (ref9) 1977; 11 chavent (ref22) 2006; 21 ref24 ref23 ref25 ref20 ref21 celeux (ref26) 1989 bock (ref11) 2000 ref29 ref7 ref4 ref6 milligan (ref30) 1996 ref5 de souza (ref27) 2004 |
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| SubjectTerms | Adaptive quadratic distances cluster interpretation indexes Clustering algorithms clustering analysis Clustering methods Data analysis Data mining Heuristic algorithms Iterative algorithms Optimization methods partition interpretation indexes Partitioning algorithms Pattern recognition Prototypes symbolic interval data analysis |
| Title | Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances |
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