BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Two-phase clustering process for outliers detection
Pattern Recognition Letters
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
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Detecting distance-based outliers in streams of data
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Tracking clusters in evolving data streams over sliding windows
Knowledge and Information Systems
Correlation-based Attribute Outlier Detection in XML
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
SPOT: A System for Detecting Projected Outliers From High-dimensional Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Correlation-based detection of attribute outliers
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
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 is widely used in many data stream application, such as network intrusion detection, fraud detection, etc. However, most existing algorithms focused on detecting class outliers and there is little work on detecting attribute outliers, which considers the correlation or relevance among the data items. In this paper we study the problem of detecting attribute outliers within the sliding windows over data streams. An efficient algorithm is proposed to perform exact outlier detection. The algorithm relies on an efficient data structure, which stores only the necessary information and can perform updates incurred by data arrival and expiration with minimum cost. To address the problem of limited memory, we also present an approximate algorithm, which selectively drops data within the current window and at the same time maintains a maximum error bound. Extensive experiments are conducted and the results show that our algorithms are efficient and effective.