Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances

被引：38

作者：

de Carvalho, Francisco de A. T. ^{[1
]}

Lechevallier, Yves ^{[2
]}

机构：

[1] Univ Fed Pernambuco, Ctr Informat, BR-50740540 Recife, PE, Brazil

[2] Inst Natl Rech Informat & Automat Paris Rocquenco, F-78153 Le Chesnay, Paris, France

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 2009年 / 39卷 / 06期

关键词：

Adaptive quadratic distances; cluster interpretation indexes; clustering analysis; partition interpretation indexes; symbolic interval data analysis; SIMILARITY;

D O I：

10.1109/TSMCA.2009.2030167

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1295 / 1306

页数：12

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