An evolving Takagi-Sugeno model based on aggregated trapezium clouds for anomaly detection in large datasets

Abstract

Anomaly detection is an important task for applications involving Big Data. Comparing with traditional method, anomaly detection in Big Data confronts growing amounts of data with high dimensionality and complex structures, which require more real-time analysis. This paper presents a fuzzy input-output system for anomalous data using electronic consumer… (More)
DOI: 10.3233/JIFS-16254

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Cite this paper

@article{Wang2017AnET, title={An evolving Takagi-Sugeno model based on aggregated trapezium clouds for anomaly detection in large datasets}, author={Meng-Xian Wang and Jianqiang Wang}, journal={Journal of Intelligent and Fuzzy Systems}, year={2017}, volume={32}, pages={2295-2308} }