M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Neighbor-based pattern detection for windows over streaming data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Distance-based outlier queries in data streams: the novel task and algorithms
Data Mining and Knowledge Discovery
The Journal of Machine Learning Research
Continuous monitoring of distance-based outliers over data streams
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
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Anomaly detection is an important data mining task, aiming at the discovery of elements that show significant diversion from the expected behavior; such elements are termed as outliers. One of the most widely employed criteria for determining whether an element is an outlier is based on the number of neighboring elements within a fixed distance (R), against a fixed threshold (k). Such outliers are referred to as distance-based outliers and are the focus of this work. In this demo, we show both an extendible framework for outlier detection algorithms and specific outlier detection algorithms for the demanding case where outlier detection is continuously performed over a data stream. More specifically: i) first we demonstrate a novel flavor of an open-source publicly available tool for Massive Online Analysis (MOA) that is endowed with capabilities to encapsulate algorithms that continuously detect outliers and ii) second, we present four online outlier detection algorithms. Two of these algorithms have been designed by the authors of this demo, with a view to improving on key aspects related to outlier mining, such as running time, flexibility and space requirements.