An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling multiple IP traffic streams with rate limits
IEEE/ACM Transactions on Networking (TON)
Symbolic dynamic analysis of complex systems for anomaly detection
Signal Processing
Learning States and Rules for Detecting Anomalies in Time Series
Applied Intelligence
An online support vector machine for abnormal events detection
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Symbolic time series analysis via wavelet-based partitioning
Signal Processing - Special section: Distributed source coding
Towards integrated and efficient scientific sensor data processing: a database approach
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
ACM Computing Surveys (CSUR)
A novel BP neural network model for traffic prediction of next generation network
ICNC'09 Proceedings of the 5th international conference on Natural computation
A neighborhood-based clustering algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Adaptive multidimensional coded modulation over flat fading channels
IEEE Journal on Selected Areas in Communications
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This article proposes a probability finite state automata-based algorithm (PFSAA) for detecting outliers of air temperature series data caused by sensor errors. This algorithm first divides the training samples of air temperature series data into subclusters that will be further used to build finite state automata by splitting and combining techniques. Then, it creates a dynamic transition matrix of PFSA based on probability theories. Finally, the outliers of the remaining test samples are detected by PFSAA. The proposed algorithm is quantitatively validated by the reference data and a traditional backpropagation neural net model. © 2012 Wiley Periodicals, Inc. Complexity, 2012 © 2012 Wiley Periodicals, Inc.