Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2015

Abstract

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.

Keywords

Least Similar Nearest Neighbours, kNN, Anomaly Detection, Ensemble

Discipline

Databases and Information Systems | Data Storage Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, New Jersey, November 14-17

First Page

1

Last Page

8

ISBN

2375-9259

Identifier

10.1109/ICDMW.2015.62

Publisher

{IEEE} Computer Society

City or Country

Atlantic City

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