电子科技大学学报2012，Vol.41Issue(1)：104-109,6.DOI:10.3969/j.issn.1001-0548.2012.01.020
基于频繁闭项集的新关联分类算法ACCF
Associative Classification Based on Closed Frequent Itemsets
摘要
Abstract
A new associative classification method named ACCF is presented based on the closed frequent itemsets. This method first mines all closed frequent itemsets and the candidate class association rules (CARs), and then constructs classifier based the selected CARs from the candidate CARS. The new instances are finally classified by a new way. Our theoretical analysis and substantial experiments on 18 datasets from UCI repository of machine learning databases show that ACCF is highly effective at classification of various kinds of datasets. Compared with the typical associative classification algorithms, ACCF can mine much less CARs and has higher average classification accuracy.关键词
关联分类/类关联规则/频繁闭项集/数据挖掘Key words
associative classification/class association rule/closed frequent itemsets/data mining分类
信息技术与安全科学引用本文复制引用
李学明,杨阳,秦东霞,周尚波..基于频繁闭项集的新关联分类算法ACCF[J].电子科技大学学报,2012,41(1):104-109,6.基金项目
国家自然科学基金(60873200) （60873200）
重庆市重大科技攻关项目(CSTC,2009AB2221) （CSTC,2009AB2221）
