A distributed unsupervised learning algorithm and its suitability to physical based observation. Issue 4 (4th July 2022)

• Record Type: Journal Article
• Authors: Hes, Radek; Gioroli, Giacomo
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 37:Issue 4(2022)
• Page Start: 443
• Page End: 455
• Publication Date: 2022-07-04
• Subjects: Distributed unsupervised learning -- distributed AI; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2022.2042536
• Languages: English
• ISSNs: 1744-5760

Abstract

Abstract : Large datasets pose a difficult challenge for clustering algorithms due to memory limitations and execution speed. Clustering is typically addressed with current popular techniques: K-Means and DBScan, which are inherently tightly coupled to all points in the data set. K-Means clustering is based on cluster centres and requires prior knowledge of the number of classes present in the dataset. DBScan relaxes this constraint but retains the need for a complete dataset during computation. In this paper, a novel 'self'-learning primitive unsupervised technique is presented that addresses the tight coupling and is readily distributable. The technique follows the comparison to class averages similar to K-Means yet relaxes the constraint of prior knowledge of the number of classes, similar to DBScan. The algorithm competes well with the standardised K-Means and DBScan variants in the context of physically based observations where Gaussian noise can be presumed. An application of usage of the unsupervised technique is presented; the classification of unknown whale species in the cook strait of New Zealand is shown to perform well. GRAPHICAL ABSTRACT: UF0001