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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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