Associative Classification over Data Streams

Based on association rules, Associative classification (AC) has shown great promise over many other classification techniques on static dataset. However, the increasing prominence of data streams arising in a wide range of advanced application has posed a new challenge for it. This paper describes a...

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Bibliographic Details
Published in2010 2nd International Conference on Information Engineering and Computer Science pp. 1 - 4
Main Authors Zhen-Hui Song, Yi Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Accuracy
Algorithm design and analysis
Association rules
Classification algorithms
Itemsets
Machine learning algorithms
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Summary:Based on association rules, Associative classification (AC) has shown great promise over many other classification techniques on static dataset. However, the increasing prominence of data streams arising in a wide range of advanced application has posed a new challenge for it. This paper describes and evaluates AC-DS, a new associative classification algorithm for data streams which is based on the estimation mechanism of the Lossy Counting (LC) and landmark window model. We apply AC-DS to mining several datasets obtained from the UCI Machine Learning Repository and the result show that the algorithm is effective and efficient.
ISBN:1424479398
9781424479399
ISSN:2156-7379
DOI:10.1109/ICIECS.2010.5678360