DynamicWEB: Adapting to Concept Drift and Object Drift in COBWEB

被引：0

作者：

Scanlan, Joel ^{[1
]}

Hartnett, Jacky ^{[1
]}

Williams, Raymond ^{[1
]}

机构：

[1] Univ Tasmania, Sch Comp & Informat Syst, Hobart, Tas 7001, Australia

来源：

AI 2008: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2008年 / 5360卷

关键词：

Data Mining; Contextual Clustering; Concept Drift;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：454 / 460

页数：7

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