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- W2492595488 abstract "Big 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." @default.
- W2492595488 created "2016-08-23" @default.
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- W2492595488 date "2016-07-01" @default.
- W2492595488 modified "2023-10-16" @default.
- W2492595488 title "Wolf search algorithm for numeric association rule mining" @default.
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- W2492595488 doi "https://doi.org/10.1109/icccbda.2016.7529549" @default.
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