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A K-means-like algorithm for informetric data clustering

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conference contribution
posted on 2015-01-01, 00:00 authored by Anna Cena, Marek Gagolewski
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.

History

Volume

89

Pagination

536-543

Location

Gijon, Spain

Open access

  • Yes

Start date

2015-06-30

End date

2015-07-03

ISSN

1951-6851

ISBN-13

978-94-62520-77-6

Language

eng

Publication classification

E1.1 Full written paper - refereed

Editor/Contributor(s)

Alonso JM, Bustince H, Reformat M

Title of proceedings

IFSA and EUSFLAT 2019 : Proceedings of the 2015 Combined Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology

Event

International Fuzzy Systems Association and European Society for Fuzzy Logic and Technology. Combined Conference (16th and 9th : 2015, Gijon, Spain)

Publisher

Atlantis Press

Place of publication

Paris, France

Series

Advances in Intelligent Systems Research

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