A K-means-like algorithm for informetric data clustering

被引：0
|
作者：
Cena, Anna [1 ]
Gagolewski, Marek [1 ,2 ]
机构：
[1] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland
[2] Warsaw Univ Technol, Fac Math & Informat Sci, PL-00662 Warsaw, Poland
来源：
PROCEEDINGS OF THE 2015 CONFERENCE OF THE INTERNATIONAL FUZZY SYSTEMS ASSOCIATION AND THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY | 2015年 / 89卷
关键词：
k-means clustering; informetrics; aggregation; impact functions;
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：
The K-means algorithm is one of the most often used clustering techniques. However, when it comes to discovering clusters in informetric data sets that consist of non-increasingly ordered vectors of not necessarily conforming lengths, such a method cannot be applied directly. Hence, in this paper, we propose a K-means-like algorithm to determine groups of producers that are similar not only with respect to the quality of information resources they output, but also their quantity.
引用
收藏
页码：536 / 543
页数：8
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