C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning - Special issue on learning with probabilistic representations
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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In this paper, we proposed an automated load patterns classification technique to detect potentially non-safe power lines to prevent the power failure and to control the power distribution system efficiently. In operating power distribution, if there is overload in the fixed load capability of a power line, the power line will be breakdown, and that accident causes significant financial damages. For the prevention of power cut, we extracted the load shape feature according to the characteristic of electric consumption in Korea, and detected the anomalous patterns of nonsafe power lines group using classifier based on EPs (emerging patterns) [1]. The discovered EPs have high support in non-safe power lines group and have low support in the normal group. In order to evaluate our classification method, power load data and information of 401 power lines are used during Feb. 2007, and compared with several existing classification methods. As a result of our experiments, the overall accuracy of EPs-based classification method about power load data was turned out to be about 91.75%. And the accuracy of non-safe power line group was about 96%.