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.
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Scanlan, J., Hartnett, J., Williams, R. (2008). DynamicWEB: Adapting to Concept Drift and Object Drift in COBWEB. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_46
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DOI: https://doi.org/10.1007/978-3-540-89378-3_46
Publisher Name: Springer, Berlin, Heidelberg
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