Author(s): Li Su | Hong-yan Liu | Zhen-Hui Song
Journal: International Journal of Modern Education and Computer Science
ISSN 2075-0161
Volume: 3;
Issue: 4;
Start page: 32;
Date: 2011;
Original page
Keywords: data streams | associative classification | frequent itemsets
ABSTRACT
Associative classification (AC) which is based on association rules has shown great promise over many other classification techniques on static dataset. Meanwhile, a new challenge have been proposed in that the increasing prominence of data streams arising in a wide range of advanced application. This paper describes and evaluates a new associative classification algorithm for data streams AC-DS, which is based on the estimation mechanism of the Lossy Counting (LC) and landmark window model. And AC-DS was applied to mining several datasets obtained from the UCI Machine Learning Repository and the result show that the algorithm is effective and efficient.
Journal: International Journal of Modern Education and Computer Science
ISSN 2075-0161
Volume: 3;
Issue: 4;
Start page: 32;
Date: 2011;
Original page
Keywords: data streams | associative classification | frequent itemsets
ABSTRACT
Associative classification (AC) which is based on association rules has shown great promise over many other classification techniques on static dataset. Meanwhile, a new challenge have been proposed in that the increasing prominence of data streams arising in a wide range of advanced application. This paper describes and evaluates a new associative classification algorithm for data streams AC-DS, which is based on the estimation mechanism of the Lossy Counting (LC) and landmark window model. And AC-DS was applied to mining several datasets obtained from the UCI Machine Learning Repository and the result show that the algorithm is effective and efficient.
