Incremental clustering for dynamic information processing
ACM Transactions on Information Systems (TOIS)
ACM Computing Surveys (CSUR)
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Distance-Based Detection and Prediction of Outliers
IEEE Transactions on Knowledge and Data Engineering
Fast Distributed Outlier Detection in Mixed-Attribute Data Sets
Data Mining and Knowledge Discovery
Online outlier detection in sensor data using non-parametric models
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Efficient Pruning Schemes for Distance-Based Outlier Detection
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
AnyOut: anytime outlier detection on streaming data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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Outlier detection has many practical applications, especially in domains that have scope for abnormal behavior, such as fraud detection, network intrusion detection, medical diagnosis, etc. In this paper, we present a technique for detecting outliers and learning from data in multi-dimensional streams. Since the concept in such streaming data may drift, learning approaches should be online and should adapt quickly. Our technique adapts to new incoming data points, and incrementally maintains the models it builds in order to overcome the effect of concept drift. Through various experimental results on real data sets, our approach is shown to be effective in detecting outliers in data streams as well as in maintaining model accuracy.