Symbolic clustering of large datasets

被引：2

作者：

Lechevallier, Yves

Verde, Rosanna ^{[1
]}

de Carvalho, Francisco de A. T. ^{[2
]}

机构：

[1] Seconda Univ Napoli, Dip Strateg Aziendali & Metod Quantit, I-81043 Capua, CE, Italy

[2] Cidade Univ, Ctr Informat, BR-50740540 Recife, PE, Brazil

来源：

DATA SCIENCE AND CLASSIFICATION | 2006年

关键词：

D O I：

10.1007/3-540-34416-0_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present an approach to cluster large datasets that integrates the Kohonen Self Organizing Maps (SOM) with a dynamic clustering algorithm of symbolic data (SCLUST). A preliminary data reduction using SOM algorithm is performed. As a result, the individual measurements are replaced by micro-clusters. These micro-clusters are then grouped in a few clusters which are modeled by symbolic objects. By computing the extension of these symbolic objects, symbolic clustering algorithm allows discovering the natural classes. An application on a real data set shows the usefulness of this methodology.

引用

页码：193 / +

页数：3

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