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DynamicWEB: Adapting to concept drift and object drift in COBWEB

Citation

Scanlan, JD and Hartnett, JS and Williams, RN, DynamicWEB: Adapting to concept drift and object drift in COBWEB, AI 2008: Advances in Artificial Intelligence 21st Australasian Joint Conference on Artificial Intelligence, 1-5 December 2008, Auckland, New Zealand, pp. 454-460. ISBN 3-540-89377-6 (2008) [Refereed Conference Paper]

Copyright Statement

Copyright 2008 Springer Berlin Heidelberg

Official URL: http://dx.doi.org/10.1007/978-3-540-89378-3_46

DOI: doi:10.1007/978-3-540-89378-3_46

Abstract

Examining concepts that change over time has been an active area of research within data mining. This paper presents a new method that functions in contexts where concept drift is present, while also allowing for modification of the instances themselves as they change over time. This method is well suited to domains where subjects of interest are sampled multiple times, and where they may migrate from one resultant concept to another. The method presented here is an extensive modification to the conceptual clustering algorithm COBWEB, and is titled DynamicWEB.

Item Details

Item Type:Refereed Conference Paper
Keywords:data mining, contextual clustering, concept drift
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Pattern Recognition and Data Mining
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
Author:Scanlan, JD (Dr Joel Scanlan)
Author:Hartnett, JS (Mrs Jacky Hartnett)
Author:Williams, RN (Dr Ray Williams)
ID Code:54002
Year Published:2008
Deposited By:Information and Communication Technology
Deposited On:2009-02-06
Last Modified:2014-12-09
Downloads:0

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