LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours

We introduce the concept of Least Similar Nearest Neighbours (LeSiNN) and use LeSiNN to detect anomalies directly. Although there is an existing method which is a special case of LeSiNN, this paper is the first to clearly articulate the underlying concept, as far as we know. LeSiNN is the first ense...

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Bibliographic Details
Published inIEEE ... International Conference on Data Mining workshops pp. 623 - 630
Main Authors Guansong Pang, Kai Ming Ting, Albrecht, David
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.11.2015
Subjects
Anomalies
Anomaly Detection
Australia
Conferences
Data models
Detectors
Ensemble
Indexing
kNN
Least Similar Nearest Neighbours
Mathematical models
Numerical models
Run time (computers)
State of the art
Time complexity
Workshops
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Summary:We introduce the concept of Least Similar Nearest Neighbours (LeSiNN) and use LeSiNN to detect anomalies directly. Although there is an existing method which is a special case of LeSiNN, this paper is the first to clearly articulate the underlying concept, as far as we know. LeSiNN is the first ensemble method which works well with models trained using samples of one instance. LeSiNN has linear time complexity with respect to data size and the number of dimensions, and it is one of the few anomaly detectors which can apply directly to both numeric and categorical data sets. Our extensive empirical evaluation shows that LeSiNN is either competitive to or better than six state-of-the-art anomaly detectors in terms of detection accuracy and runtime.
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SourceType-Conference Papers & Proceedings-2
ISSN:2375-9259
DOI:10.1109/ICDMW.2015.62