Mining Frequent Ranges of Numeric Attributes via Ant Colony Optimization for Continuous Domains without Specifying Minimum Support
نویسندگان
چکیده
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 of a dataset would be interesting. In this paper a new approach to finding frequent intervals of numeric attributes is presented using Ant Colony Optimization for Continuous domains (ACOR). The results show that this approach leads to more precise and accurate intervals in comparison with other approaches like discretizing into intervals with equal lengths. KeywordsNumeric Association Rule Mining; Preprocessing; Ant Colony Optimization; Data Mining
منابع مشابه
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 h...
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