posted on 2015-01-01, 00:00authored byAnna 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.
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)