MOCANAR: A Multi-Objective Cuckoo Search Algorithm for Numeric Association Rule Discovery

摘要

Extracting association rules from numeric features involves searching a very large search space. Todeal with this problem, in this paper a meta-heuristic algorithm is used that we have calledMOCANAR. The MOCANAR is a Pareto based multi-objective cuckoo search algorithm whichextracts high quality association rules from numeric datasets. The support, confidence,interestingness and comprehensibility are the objectives that have been considered in theMOCANAR. The MOCANAR extracts rules incrementally, in which, in each run of the algorithm, asmall number of high quality rules are made. In this paper, a comprehensive taxonomy of metaheuristicalgorithm have been presented. Using this taxonomy, we have decided to use a CuckooSearch algorithm because this algorithm is one of the most matured algorithms and also, it is simpleto use and easy to comprehend. In addition, until now, to our knowledge this method has not beenused as a multi-objective algorithm and has not been used in the association rule mining area. Todemonstrate the merit and associated benefits of the proposed methodology, the methodology hasbeen applied to a number of datasets and high quality results in terms of the objectives wereextracted.