Please use this identifier to cite or link to this item: http://hdl.handle.net/10321/3001
Title: Kestrel-based search algorithm for association rule mining and classification of frequently changed items
Authors: Agbehadji, Israel Edem 
Fong, Simon 
Millham, Richard 
Keywords: Kestrel-based search algorithm;Association rule mining;Classification;Frequently changed items;Big data environment
Issue Date: 2016
Publisher: IEEE
Source: Agbehadji, I.E. et al. 2016. Kestrel-based search algorithm for association rule mining and classification of frequently changed items. 2016 8th International Conference on Computational Intelligence and Communication Networks. IEEE, 356-360. DOI 10.1109/CICN.2016.76
Abstract: Nature inspired approaches have been used in the design of computer solutions for real life problems. These computer solutions take the form of algorithms which characterize specific behaviour of animals or birds in their natural habitat. The two bio-inspired computational concepts in modern times includes evolutionary and swarm intelligence. A novel introduction to the bio-inspired computational concepts of swarm behaviour is the study of characteristics of kestrel birds. The study presents, as a concept paper, a meta-heuristic algorithm called kestrel-based search algorithm (KSA) for association rule mining and classification of frequently changed items on big data environment. This algorithm aims to find best possible rules and patterns in dataset using minimum support and minimum confidence.
URI: http://hdl.handle.net/10321/3001
ISBN: 2472-7555
Appears in Collections:Research Publications (Accounting and Informatics)

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