A probabilistic resource allocating network for novelty detection
Neural Computation
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Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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Information Visualization
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
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Known Unknowns: Novelty Detection in Condition Monitoring
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Finding anomalous periodic time series
Machine Learning
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Efficient discovery of unusual patterns in time series
New Generation Computing
Efficient anomaly monitoring over moving object trajectory streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of Time Series Novelty Detection Strategies for Synthetic and Real Data
Neural Processing Letters
Relational Frequent Patterns Mining for Novelty Detection from Data Streams
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Novelty Detection from Evolving Complex Data Streams with Time Windows
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Real-time detection of task switches of desktop users
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Discovering novelty in spatio/temporal data using one-class support vector machines
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Reduced data similarity-based matching for time series patterns alignment
Pattern Recognition Letters
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
A review on time series data mining
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ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A random method for quantifying changing distributions in data streams
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Granularity adaptive density estimation and on demand clustering of concept-drifting data streams
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Temporal data mining for smart homes
Designing Smart Homes
Distance-Based outliers in sequences
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
Anomalistic sequence detection
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See what's enBlogue: real-time emergent topic identification in social media
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Robust neural network for novelty detection on data streams
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Time series discord discovery using WAT algorithm and iSAX representation
Proceedings of the Third Symposium on Information and Communication Technology
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Probabilistic distance based abnormal pattern detection in uncertain series data
Knowledge-Based Systems
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Energy-based function to evaluate data stream clustering
Advances in Data Analysis and Classification
Adaptive fuzzy clustering based anomaly data detection in energy system of steel industry
Information Sciences: an International Journal
Review: A review of novelty detection
Signal Processing
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In this paper, we present a new framework for online novelty detection on temporal sequences. This framework include a mechanism for associating each detection result with a confidence value. Based on this framework, we develop a concrete online detection algorithm, by modeling the temporal sequence using an online support vector regression algorithm. Experiments on both synthetic and real world data are performed to demonstrate the promising performance of our proposed detection algorithm.