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Clustering in large data sets with the limited memory bundle method

- Karmitsa, Napsu, Bagirov, Adil, Taheri, Sona

  • 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
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  • 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.

Nonsmooth DC programming approach to the minimum sum-of-squares clustering problems

- Bagirov, Adil, Taheri, Sona, Ugon, Julien

  • Authors: Bagirov, Adil , Taheri, Sona , Ugon, Julien
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Pattern Recognition Vol. 53, no. (2016), p. 12-24
  • Relation: http://purl.org/au-research/grants/arc/DP140103213
  • Full Text: false
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  • Description: This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using their difference of convex representations. A non-smooth non-convex optimization formulation of the clustering problem is used to design the algorithm. Characterizations of critical points, stationary points in the sense of generalized gradients and inf-stationary points of the clustering problem are given. The proposed algorithm is tested and compared with other clustering algorithms using large real world data sets. © 2015 Elsevier Ltd. All rights reserved.

Cyberattack triage using incremental clustering for intrusion detection systems

- Taheri, Sona, Bagirov, Adil, Gondal, Iqbal, Brown, Simon

  • Authors: Taheri, Sona , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
  • Date: 2020
  • Type: Text , Journal article
  • Relation: International Journal of Information Security Vol. 19, no. 5 (2020), p. 597-607
  • Relation: http://purl.org/au-research/grants/arc/DP190100580
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  • Description: Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
  • Description: This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580).
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