Comparing metries in fuzzy clustering for symbolic data on SODAS format

被引：0
|
作者：
Silva, A [1 ]
Carvalho, F [1 ]
Ludermir, T [1 ]
Cavalcanti, N [1 ]
机构：
[1] Univ Fed Pernambuco, CIn, BR-52011030 Recife, PE, Brazil
来源：
ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2004 | 2004年 / 3315卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A number of approaches to solve the problem of data clustering are available in the literature. This paper introduces a comparative study in some distances metrics very known in the literature of symbolic data. This work uses an adaptation of an algorithm that applies concepts of fuzzy clustering and then creates groups of individuals characterized by symbolic variables of mixed types. The core of the algorithm consists of a dissimilarity function that can be replaced without collateral effects. The input data is provided on the format of a SODAS file. The results of the experiments on representative databases show the performance of each analyzed metric.
引用
收藏
页码：727 / 736
页数：10
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