Xiufang Wang
School of Electrical Information Engineering, Northeast Petroleum University, Daqing, China
Yan Wang
School of Electrical Information Engineering, Northeast Petroleum University, Daqing, China
Hongbo Bi
School of Electrical Information Engineering, Northeast Petroleum University, Daqing, China
Running Gao
180 w. Big Springs Rood. Apt.23, Riverside, California, PC: 92507
ABSTRACT
At present, a massive portion of data stored in the heat-supply network management system has formed the data grave which does not embody the intrinsic properties of data. To solve this problem, it is particularly important to take effective mining methods which reuse the existing historical data to improve the current system. In this paper, we first dispersed the continuous attribute information based on both entropy and importance of attribute to preprocess the data of heat-supply network. Then we exploited three kinds of algorithm for data mining, namely, classification and prediction based on the decision tree, cluster analysis based on the K-mean partition and association rules mining based on the frequent itemset model. Finally, we established forecasting model combining the results of three aforementioned mining schemes. The model was then embedded into the prediction module of present system and the results demonstrated the proposed scheme can improve the prediction performance efficiently.
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How to cite this article
Xiufang Wang, Yan Wang, Hongbo Bi and Running Gao, 2013. Heat-Supply Network State Prediction Based on Optimum Combination
Model of Data Mining. Journal of Applied Sciences, 13: 2443-2449.
DOI: 10.3923/jas.2013.2443.2449
URL: https://scialert.net/abstract/?doi=jas.2013.2443.2449
DOI: 10.3923/jas.2013.2443.2449
URL: https://scialert.net/abstract/?doi=jas.2013.2443.2449
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