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Resource-aware knowledge discovery in data streams
Monash University, Melbourne, VIC.
Monash University, Melbourne, VIC.
2004 (English)In: Proceedings of the First International Workshop on Knowledge Discovery in Data Streams / [ed] J. Gama; J.S. Aguilar-Ruiz, ECML/PKDD 2004 conference , 2004Conference paper, Published paper (Refereed)
Abstract [en]

Mining data streams has raised a number of research challenges for the data mining community. These challenges include the limitations of computational resources, especially because mining streams of data most likely be done on a mobile device with limited resources. Also due to the continuality of data streams, the algorithm should have only one pass or less over the incoming data elements. In this paper, our Algorithm Output Granularity (AOG) approach in mining data streams is discussed. AOG is a novel adaptable approach that can cope with the challenging inherent features of data streams. We also show the results for AOG based clustering in a resource constrained environment.

Place, publisher, year, edition, pages
ECML/PKDD 2004 conference , 2004.
Identifiers
URN: urn:nbn:se:ltu:diva-38920Local ID: d79c7ba0-da39-11dc-b464-000ea68e967bOAI: oai:DiVA.org:ltu-38920DiVA, id: diva2:1012427
Conference
International Workshop on Knowledge Discovery in Data Streams : 24/09/2004 - 24/09/2004
Note
Uppr├Ąttat; 2004; 20080213 (ysko)Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2017-11-25Bibliographically approved

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Zaslavsky, Arkady

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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Output format
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