The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Predicting equity returns from securities data
Advances in knowledge discovery and data mining
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Methodological and practical aspects of data mining
Information and Management
The reliability issue of computer-aided breast cancer diagnosis
Computers and Biomedical Research
Business applications of data mining
Communications of the ACM - Evolving data mining into solutions for insights
Book review: Three perspectives of data mining
Artificial Intelligence
Knowledge Discovery with Clustering Based on Rules. Interpreting Results
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Signal extraction and knowledge discovery based on statistical modeling
Theoretical Computer Science - Algorithmic learning theory
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Power load forecasting using support vector machine and ant colony optimization
Expert Systems with Applications: An International Journal
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
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Considering the importance of the peak load to the dispatching and management of the electric system, the error of peak load is proposed in this paper as criteria to evaluate the effect of the forecasting model. This paper proposes a systemic framework that attempts to use data mining and knowledge discovery (DMKD) to pretreat the data. And a new model is proposed which combines artificial neural networks with data mining and knowledge discovery for electric load forecasting. With DMKD technology, the system not only could mine the historical daily loading which had the same meteorological category as the forecasting day to compose data sequence with highly similar meteorological features, but also could eliminate the redundant influential factors. Then an artificial neural network is constructed to predict according to its characteristics. Using this new model, it could eliminate the redundant information, accelerate the training speed of neural network and improve the stability of the convergence. Compared with single BP neural network, this new method can achieve greater forecasting accuracy.