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Which Outlier Detector Should I use?

Version 2 2024-06-05, 02:47
Version 1 2019-04-28, 10:37
conference contribution
posted on 2024-06-05, 02:47 authored by KM Ting, Sunil AryalSunil Aryal, T Washio
This tutorial has four aims: (1) Providing the current comparative works on different outlier detectors, and analysing the strengths and weaknesses of these works and their recommendations. (2) Presenting non-obvious applications of outlier detectors. This provides examples of how outlier detectors are used in areas which are not normally considered to be the domains of outlier detection. (3) Inviting the research community to explore future research directions, in terms of both comparative study and outlier detection in general. (4) Giving an advice on the factors to consider when choosing an outlier detector, and strengths and weaknesses of some "top" recommended algorithms based on the current understanding in the literature.

History

Volume

2018-November

Pagination

8-8

Location

Singapore, Singapore

Start date

2018-11-17

End date

2018-11-20

ISSN

2374-8486

ISBN-13

9781538691588

Language

English

Notes

keywords: data mining;recommender systems;outlier detection;nonobvious applications;recommended algorithms;Detectors;Machine learning;Anomaly detection;Australia;Tutorials;Kernel;Anomaly Detection tutorial

Publication classification

E3.1 Extract of paper

Title of proceedings

2018 IEEE International Conference on Data Mining (ICDM)

Event

ICDM 2018

Publisher

IEEE

Series

IEEE International Conference on Data Mining