摘要
孤立点通常都包含着重要的信息,挖掘出孤立点的内涵知识可以帮助用户更好地认知数据。通过给出的孤立点的原因属性子空间及其孤立度和孤立点的相似度等概念,提出了一个基于属性子空间的孤立点内涵知识挖掘算法,得到了每个孤立点的原因属性集,并结合聚类的思想把孤立点按照其相似性特征进行了分类,使每一类中的所有孤立点在一定精度下都具有相同的原因属性集。实验结果表明该算法是有效和实用的,且易用性较强。
Outliers usually contain important information,it can help improving the users＇ understanding of the data.New definitions of cause attribute subspace of outliers,degree of cause attribute subspace and similarity of outliers were given,and then an algorithm for finding intentional knowledge of outliers based on attribute subspace was proposed,the approach can obtain the cause attributes set of every outlier.Then the outliers were classified by their similarity combined with the thinking of clustering,all the outliers of every class have the same cause attributes set under certain precision.The experiment results show that the algorithm is effective and practical,and more ease of use.
出处
《计算机科学》
CSCD
北大核心
2011年第3期199-202,共4页
Computer Science
基金
重庆市科技攻关资金项目(CSTC
2009AB2049CSTC
2009AC2068)资助
关键词
孤立点
属性子空间
孤立点相似度
内涵知识
Outliers
Attribute subspace
Outliers similarity
Intentional knowledge
