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 Type:	Refereed Conference Paper
Keywords:	data mining, contextual clustering, concept drift
Research Division:	Information and Computing Sciences
Research Group:	Computer vision and multimedia computation
Research Field:	Pattern recognition
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Information systems, technologies and services not elsewhere classified
UTAS Author:	Scanlan, JD (Dr Joel Scanlan)
UTAS Author:	Hartnett, JS (Mrs Jacky Hartnett)
UTAS 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
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