Wolf search algorithm for numeric association rule mining
Agbehadji I.E.2; Fong S.1; Millham R.2
Source PublicationProceedings of 2016 IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2016
AbstractBig data has become one of the key sources for valuable information and as information becomes larger it poses some computational challenge in finding a best possible solution for mining association rules and discovering patterns in data. Meta-heuristic algorithm when applied to mining association rules aims to find best possible rules from data without being stuck in local optimal. Example of meta-heuristics algorithm includes genetic algorithm and particle swarm optimization algorithm. Finding appropriate representation of various types of patterns using rough numerical values attributes is still a challenge because most association rules cannot be applied to numerical data without discretization which may lead to information loss. Mining numeric association rules is a hard optimization problem rather than being a discretization, thus, this paper proposes a new meta-heuristic algorithm which uses wolf search algorithm (WSA) for numeric association rule mining from rough values within tolerable ranges.
Keywordbig data Numeric association rule mining particle swarm optimization algorithm Wolf search algorithm
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.Durban University of Technology
Recommended Citation
GB/T 7714
Agbehadji I.E.,Fong S.,Millham R.. Wolf search algorithm for numeric association rule mining[C],2016:146-151.
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