A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Clustering in large data sets with the limited memory bundle method
Tekijät: Napsu Karmitsa, Adil M. Bagirov, Sona Taheri
Kustantaja: ELSEVIER SCI LTD
Julkaisuvuosi: 2018
Journal: Pattern Recognition
Tietokannassa oleva lehden nimi: PATTERN RECOGNITION
Lehden akronyymi: PATTERN RECOGN
Vuosikerta: 83
Aloitussivu: 245
Lopetussivu: 259
Sivujen määrä: 15
ISSN: 0031-3203
eISSN: 1873-5142
DOI: https://doi.org/10.1016/j.patcog.2018.05.028
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/35725660
The aim of this paper is to design an algorithm based on nonsmooth optimization techniques to solve the minimum sum-of-squares clustering problems in very large data sets. First, the clustering problem is formulated as a nonsmooth optimization problem. Then the limited memory bundle method [Haarala et al., 2007] is modified and combined with an incremental approach to design a new clustering algorithm. The algorithm is evaluated using real world data sets with both the large number of attributes and the large number of data points. It is also compared with some other optimization based clustering algorithms. The numerical results demonstrate the efficiency of the proposed algorithm for clustering in very large data sets.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
