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Research Publications (Accounting and Informatics)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/212

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Author: search.filters.author.Millham, Richard

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    Theme evolution and structure in Twitter : a case study of South African student protests of 2015
    (IEEE, 2016) Millham, Richard
    Social media, based on human interactions, often has constantly changing foci, or themes, within their interactions. These themes, frequently used to categorize information within this social media, often evolve dependent on time, domain, and event contexts. Using a case study of South African student protests during a short but eventful time period in 2015, this paper analyses numerous tweets made to selected hashtags (one national and two local) in order to determine relevant themes within these tweets and to determine how these themes evolved, both at the national and local level, given their context. It was discovered that, as certain events unfolded, certain themes varied in prominence and locally-based hashtags converged into nationally-based hashtags reflecting a change in the nature of the protests.
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    Kestrel-based search algorithm for association rule mining and classification of frequently changed items
    (IEEE, 2016) Agbehadji, Israel Edem; Fong, Simon; Millham, Richard
    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.