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Anomaly and event detection for unsupervised athlete performance data

O'Donoghue, Jim, Roantree, Mark, Cullen, Bryan, Moyna, Niall orcid logoORCID: 0000-0003-1061-8528, O'Sullivan, Conor and McCarren, Andrew orcid logoORCID: 0000-0002-7297-0984 (2015) Anomaly and event detection for unsupervised athlete performance data. In: LWA 2015: Knowledge Discovery and Machine Learning stream, 7-9 Oct 2015, Trier, Germany.

Abstract
There are many projects today where data is collected automatically to provide input for various data mining algorithms. A problem with freshly generated datasets is their unsupervised nature, leading to difficulty in fitting predictive algorithms without substantial manual effort. One of the first steps in dataset preparation and mining is anomaly detection, where clear anomalies and outliers as well as events or changes in the pattern of the data are identified as a precursor to subsequent steps in data mining. In the research presented here, we provide a multi-step anomaly detection process which utilises different combinations of algorithms for the most accurate identification of outliers and events.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Proceedings of the {LWA} 2015 Workshops: KDML, FGWM, IR, and FGDB, Trier, Germany, October 7-9, 2015. {CEUR} Workshop Proceedings 1458. CEUR-WS.org.
Publisher:CEUR-WS.org
Official URL:http://ceur-ws.org/Vol-1458/E27_CRC63_Odonoghue.pd...
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Framework Programme 7
ID Code:20867
Deposited On:23 Oct 2015 10:26 by Jim O'Donoghue . Last Modified 26 Jun 2019 10:33
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