Multi-objective Numeric Association Rules Mining via Ant Colony Optimization for Continuous Domains without Specifying Minimum Support and Minimum Confidence

Currently, all search algorithms which use discretization of numeric attributes for numeric association rule mining, work in the way that the original distribution of the numeric attributes will be lost. This issue leads to loss of information, so that the association rules which are generated through this process are not precise and accurate. Based on this fact, algorithms which can natively handle numeric attributes would be interesting. Since association rule mining can be considered as a multiobjective problem, rather than a single objective one, a new multi-objective algorithm for numeric association rule mining is presented in this paper, using Ant Colony Optimization for Continuous domains (ACOR). This algorithm mines numeric association rules without any need to specify minimum support and minimum confidence, in one step. In order to do this we modified ACOR for generating rules. The results show that we have more precise and accurate rules after applying this algorithm and the number of rules is more than the ones resulted from previous works. KeywordsAnt Colony Optimization for Continuous Domains, Numeric association rules mining, Multi objective association rules mining