IntelliClean: a knowledge-based intelligent data cleaner
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Maximum entropy for collaborative filtering
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Fuzzy logic-based expert system to predict the results of finite element analysis
Knowledge-Based Systems
Decentralized multi-objective bilevel decision making with fuzzy demands
Knowledge-Based Systems
An intelligent agent-based architecture for strategic information system applications
Knowledge-Based Systems
Drag-free and attitude control for the GOCE satellite
Automatica (Journal of IFAC)
An axiomatic approach to soft learning vector quantization and clustering
IEEE Transactions on Neural Networks
Computers in Biology and Medicine
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There are a number of dirty data in observation data set derived from ocean observing network. These data should be carefully and reasonably processed before they are used for forecasting or analysis in oceanic warning system (OWS). Due to high-dimensional and dynamic oceanic data, we propose an intelligent integrated data processing model for the OWS. Firstly, we design an integrated framework of the oceanic data processing and present its processing model. The function of each module of this model is analyzed in details. Then, we propose several intelligent data processing methods, such as an intelligent data cleaning method based on the fuzzy c-means algorithm, a data filtering and clustering method based on the greedy clustering algorithm, and a data processing method based on the maximum entropy for the OWS. The efficiency and accuracy of the proposed model is proved by experimental results of observation data of the Red Tide. The proposed model can automatically find the new clustering center with the updated sample data, and outperforms several algorithms in data processing for the OWS.