Mining Frequent Ranges of Numeric Attributes via Ant Colony Optimization for Continuous Domain without Specifying Minimum Support
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.
Keywords: Numeric Association Rule Mining; Preprocessing; Ant Colony Optimization; Data Mining
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ABOUT THE AUTHORS
Parisa Moslehi
MSc of Computer engineering
Behrouz Minaei
Assistant Professor of Iran University of Science and Technology
Mahdi Nasiri
Ph.D student of Iran University of Science and Technology
Erfan Nazari Fazel
Ms.c
Parisa Moslehi
MSc of Computer engineering
Behrouz Minaei
Assistant Professor of Iran University of Science and Technology
Mahdi Nasiri
Ph.D student of Iran University of Science and Technology
Erfan Nazari Fazel
Ms.c
