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A framework for initialising a dynamic clustering algorithm: ART2-A

Chambers, SJ and Jarman, I and Lisboa, P (2015) A framework for initialising a dynamic clustering algorithm: ART2-A. In: Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on . pp. 273-280. (Computational Intelligence and Data Mining (CIDM), 9th-12th December 2014, Orlando, Florida).

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Abstract

Algorithms in the Adaptive Resonance Theory (ART) family adapt to structural changes in data as new information presents, making it an exciting candidate for dynamic online clustering of big health data. Its use however has largely been restricted to the signal processing field. In this paper we introduce an refinement of the ART2-A method within an adapted separation and concordance (SeCo) framework which has been shown to identify stable and reproducible solutions from repeated initialisations that also provides evidence for an appropriate number of initial clusters that best calibrates the algorithm with the data presented. The results show stable, reproducible solutions for a mix of real-world heath related datasets and well known benchmark datasets, selecting solutions which better represent the underlying structure of the data than using a single measure of separation. The scalability of the method and it's facility for dynamic online clustering makes it suitable for finding structure in big data.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Applied Mathematics
Publisher: IEEE
Date Deposited: 30 Oct 2015 14:16
Last Modified: 07 Oct 2016 10:36
DOI or Identification number: 10.1109/CIDM.2014.7008678
URI: http://researchonline.ljmu.ac.uk/id/eprint/2267

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