A K-means-like algorithm for informetric data clustering

DOI

10.2991/ifsa-eusflat-15.2015.77How to use a DOI?

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

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Anna Cena
AU  - Marek Gagolewski
PY  - 2015/06
DA  - 2015/06
TI  - A K-means-like algorithm for informetric data clustering
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 536
EP  - 543
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.77
DO  - 10.2991/ifsa-eusflat-15.2015.77
ID  - Cena2015/06
ER  -