Clustering in large data sets with the limited memory bundle method

: Napsu Karmitsa, Adil M. Bagirov, Sona Taheri

Publisher: ELSEVIER SCI LTD

: 2018

: Pattern Recognition

: PATTERN RECOGNITION

: PATTERN RECOGN

: 83

: 245

: 259

: 15

: 0031-3203

: 1873-5142

DOI: https://doi.org/10.1016/j.patcog.2018.05.028

: 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.

lmbmclust (1).pdf