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DynamicWEB: Adapting to concept drift and object drift in COBWEB
conference contribution
posted on 2023-05-23, 04:15 authored by Joel ScanlanJoel Scanlan, Hartnett, JS, Williams, RNExamining 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.
History
Publication title
AI 2008: Advances in Artificial Intelligence 21st Australasian Joint Conference on Artificial IntelligenceVolume
Lecture Notes in Computer Science 5360Editors
Wobcke, W & Zhang, MPagination
454-460ISBN
3-540-89377-6Department/School
School of Information and Communication TechnologyPublisher
Springer-VerlagPlace of publication
Berlin, GermanyEvent title
AJCAIEvent Venue
Auckland, New ZealandDate of Event (Start Date)
2008-12-01Date of Event (End Date)
2008-12-05Rights statement
Copyright 2008 Springer Berlin HeidelbergRepository Status
- Restricted