List of Titles

Authors: Karmitsa, Napsu , Bagirov, Adil , Taheri, Sona
Date: 2018
Type: Text , Journal article
Relation: Pattern Recognition Vol. 83, no. (2018), p. 245-259
Relation: http://purl.org/au-research/grants/arc/DP140103213
Full Text: false
Reviewed:
Description: 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.

Authors: Bagirov, Adil , Taheri, Sona , Asadi, Soodabeh
Date: 2019
Type: Text , Journal article
Relation: Journal of Industrial and Management Optimization Vol. 15, no. 2 (2019), p. 909-932
Relation: http://purl.org/au-research/grants/arc/DP140103213
Full Text: false
Reviewed:
Description: The problem of finding a continuous piecewise linear function approximating a regression function is considered. This problem is formulated as a nonconvex nonsmooth optimization problem where the objective function is represented as a difference of convex (DC) functions. Subdifferentials of DC components are computed and an algorithm is designed based on these subdifferentials to find piecewise linear functions. The algorithm is tested using some synthetic and real world data sets and compared with other regression algorithms.

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