A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised Profiling for Identifying Superimposed Fraud
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
Eigenspace-based anomaly detection in computer systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Network-Based Problem Detection for Distributed Systems
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Dynamic syslog mining for network failure monitoring
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Unifying Framework for Detecting Outliers and Change Points from Time Series
IEEE Transactions on Knowledge and Data Engineering
An annotation method for sensor data streams based on statistical patterns
DBA'06 Proceedings of the 24th IASTED international conference on Database and applications
On-line alert systems for production plants: A conflict based approach
International Journal of Approximate Reasoning
Multi-scale anomaly detection algorithm based on infrequent pattern of time series
Journal of Computational and Applied Mathematics
Land cover change detection: a case study
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting outlier samples in multivariate time series dataset
Knowledge-Based Systems
Technology extraction from time series data reflecting expert operator skills and knowledge
International Journal of Computer Applications in Technology
Technology Extraction of Expert Operator Skills from Process Time Series Data
Learning Classifier Systems
Adaptive burst detection in a stream engine
Proceedings of the 2009 ACM symposium on Applied Computing
Incremental outlier detection in data streams using local correlation integral
Proceedings of the 2009 ACM symposium on Applied Computing
A method to implement effective My-page service system using three-dimensional vectors
International Journal of Computer Applications in Technology
Network anomaly detection based on Eigen equation compression
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A comprehensive survey of numeric and symbolic outlier mining techniques
Intelligent Data Analysis
Level change detection in time series using higher order statistics
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Predicting service request rates for adaptive resource allocation in SOA
Proceedings of the International Workshop on Enterprises & Organizational Modeling and Simulation
An incident analysis system NICTER and its analysis engines based on data mining techniques
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
A review on time series data mining
Engineering Applications of Artificial Intelligence
Efficient algorithms for finding frequent substructures from semi-structured data streams
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
Effective dimension in anomaly detection: its application to computer systems
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
Real-time change-point detection using sequentially discounting normalized maximum likelihood coding
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Multi-stage change-point detection scheme for large-scale simultaneous events
Computer Communications
Visualization of cluster changes by comparing self-organizing maps
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Change detection in time series data using wavelet footprints
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
A fuzzy index for detecting spatiotemporal outliers
Geoinformatica
Stock fraud detection using peer group analysis
Expert Systems with Applications: An International Journal
Incremental connectivity-based outlier factor algorithm
VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference
Sequential change-point detection based on direct density-ratio estimation
Statistical Analysis and Data Mining
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Change-point detection with feature selection in high-dimensional time-series data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We are concerned with the issues of outlier detection and change point detection from a data stream. In the area of data mining, there have been increased interest in these issues since the former is related to fraud detection, rare event discovery, etc., while the latter is related to event/trend by change detection, activity monitoring, etc. Specifically, it is important to consider the situation where the data source is non-stationary, since the nature of data source may change over time in real applications. Although in most previous work outlier detection and change point detection have not been related explicitly, this paper presents a unifying framework for dealing with both of them on the basis of the theory of on-line learning of non-stationary time series. In this framework a probabilistic model of the data source is incrementally learned using an on-line discounting learning algorithm, which can track the changing data source adaptively by forgetting the effect of past data gradually. Then the score for any given data is calculated to measure its deviation from the learned model, with a higher score indicating a high possibility of being an outlier. Further change points in a data stream are detected by applying this scoring method into a time series of moving averaged losses for prediction using the learned model. Specifically we develop an efficient algorithms for on-line discounting learning of auto-regression models from time series data, and demonstrate the validity of our framework through simulation and experimental applications to stock market data analysis.