A K-means-like algorithm for informetric data clustering
- https://doi.org/10.2991/ifsa-eusflat-15.2015.77How to use a DOI?
- K-means clustering, informetrics, aggregation, impact functions.
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/ifsa-eusflat-15.2015.77 ID - Cena2015/06 ER -