THE CHOICE OF METRICS FOR CLUSTERING ALGORITHMS

被引：0

作者：

Grabusts, Peter ^{[1
]}

机构：

[1] Rezekne Higher Educ Inst, Atbrivoshanas Al 90, LV-4601 Rezekne, Latvia

来源：

ENVIRONMENT, TECHNOLOGY, RESOURCES, PROCEEDINGS OF THE 8TH INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE, 2011, VOL II | 2011年

关键词：

metric; clustering algorithms;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Methods of data analysis and automatic processing are treated as knowledge discovery. In many cases it is necessary to classify, data in some way or find regularities in the data. That is why the notion of similarity is becoming more and more important in the context of intelligent data processing systems. It is frequently required to ascertain how the data are interrelated, how various data differ or agree with each other, and what the measure of their comparison is. An important part in detection of similarity in clustering algorithms play the accuracy in the choice of metrics and the correctness of the clustering algorithms operation.

引用

页码：70 / 76

页数：7

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