Particle swarm Optimization Based Association Rule Mining
نویسنده
چکیده
Association rule mining is one of the widely using and simple concepts to find the frequent item sets from large number of datasets. While generating frequent item sets from a large dataset using association rule mining is not so efficient. This can be improved by using particle swarm optimization algorithm (PSO). PSO algorithm is population based evolutionary heuristic search methods used for solving different combinatorial problems. In general the rule generated by association rule mining technique do not consider the negative occurrences of attributes in them, but by using PSO algorithm over these rules the system can predict the rules which contains negative attributes.
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