计算机工程与科学2017，Vol.39Issue(10)：1966-1970,5.DOI:10.3969/j.issn.1007-130X.2017.10.028
一种改进的关联分类算法
An improved associative classification algorithm
摘要
Abstract
The associative classification algorithm based on support and confidence is an important classification algorithm in data mining.This algorithm discovers frequent item sets and generates rules according to the threshold of confidence.However,the rules are of low quality.To address the problem,we propose an improved associative classification (AIAC) algorithm.Firstly,the AIAC selects a large number of attribute-value pairs to build small data sets.Secondly,the body of each rule is made up of the best attribute-value pairs picked from the small data sets.Finally,the AIAC employs the instance covering technique to cover all of the instances in small data sets,and builds a high quality classifier.Experimental results on 25 UCI datasets show that the AIAC can achieve much higher classification accuracy.关键词
数据挖掘/关联分类/支持度/置信度/分类准确率Key words
data mining/associative classification/support/confidence/classification accuracy分类
信息技术与安全科学引用本文复制引用
全秀祥,周忠眉,黄再祥..一种改进的关联分类算法[J].计算机工程与科学,2017,39(10):1966-1970,5.基金项目
福建省自然科学基金(2013J01259) （2013J01259）
国家自然科学基金(61170129) （61170129）
福建省中青年教师教育科研项目(JA15303) （JA15303）
