title:
 
A K-means-like algorithm for informetric data clustering
publication:
 
eusflat-15
ISBN:
  978-94-62520-77-6
ISSN:
  1951-6851
DOI:
  doi:10.2991/ifsa-eusflat-15.2015.77 (how to use a DOI)
author(s):
 
Anna Cena, Marek Gagolewski
corresponding author:
 
Anna Cena
publication date:
 
June 2015
keywords:
 
K-means clustering, informetrics, aggregation, impact functions.
abstract:
 
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.
copyright:
 
© The authors.
This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
full text: