Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ADMIT: anomaly-based data mining for intrusions
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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ACM SIGMOD Record
Research issues in data stream association rule mining
ACM SIGMOD Record
Online outlier detection in sensor data using non-parametric models
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Automatic outlier detection for time series: an application to sensor data
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Malicious Node Detection in Wireless Sensor Networks Using an Autoregression Technique
ICNS '07 Proceedings of the Third International Conference on Networking and Services
Detecting distance-based outliers in streams of data
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Detecting Current Outliers: Continuous Outlier Detection over Time-Series Data Streams
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
The hitchhiker's guide to successful wireless sensor network deployments
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Stop Chasing Trends: Discovering High Order Models in Evolving Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Detection of unique temporal segments by information theoretic meta-clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
DBOD-DS: distance based outlier detection for data
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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Outlier detection is a well established area of statistics but most of the existing outlier detection techniques are designed for applications where the entire dataset is available for random access. A typical outlier detection technique constructs a standard data distribution or model and identifies the deviated data points from the model as outliers. Evidently these techniques are not suitable for online data streams where the entire dataset, due to its unbounded volume, is not available for random access. Moreover, the data distribution in data streams change over time which challenges the existing outlier detection techniques that assume a constant standard data distribution for the entire dataset. In addition, data streams are characterized by uncertainty which imposes further complexity. In this paper we propose an adaptive, online outlier detection technique addressing the aforementioned characteristics of data streams, called Adaptive Outlier Detection for Data Streams (A-ODDS), which identifies outliers with respect to all the received data points as well as temporally close data points. The temporally close data points are selected based on time and change of data distribution. We also present an efficient and online implementation of the technique and a performance study showing the superiority of A-ODDS over existing techniques in terms of accuracy and execution time on a real-life dataset collected from meteorological applications.