一种改进的关联分类算法

摘要

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.%基于支持度-置信度的关联分类是一项重要的分类算法,这种关联分类算法先构建频繁项集,然后通过置信度的阈值来选取规则,容易产生质量不高的规则.针对这个问题,提出了一种改进关联分类算法:首先,选取大量的属性值对建立起条件小训练集;其次,每条规则主体通过选取条件小训练集中最好属性值对连接生成;最后,采用实例覆盖技术覆盖小训练集的每个实例,构建具有较高质量的分类器.在25个UCI数据集上的实验结果表明,所提出的改进关联分类算法的准确率得到了显著提高.