DynamicWEB: Adapting to Concept Drift and Object Drift in COBWEB

Authors:
Joel Scanlan;Jacky Hartnett;Raymond Williams
Affiliations:
School of Computing and Information Systems, University of Tasmania, Tasmania, Australia;School of Computing and Information Systems, University of Tasmania, Tasmania, Australia;School of Computing and Information Systems, University of Tasmania, Tasmania, Australia
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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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 due to Object Drift. The method presented here is an extensive modification to the conceptual clustering algorithm COBWEB, and is titled DynamicWEB.