计算机应用研究2016，Vol.33Issue(6)：1633-1636,4.DOI:10.3969/j.issn.1001-3695.2016.06.008
基于 MDLP-Apriori 算法的离散 Shannon 熵值标签排序
MDLP-Apriori algorithm based discrete Shannon entropy for label ranking
于磊 1王普 1赵寒 1翁壮1
作者信息
- 1. 北京工业大学 电子信息与控制工程学院，北京 100124
- 折叠
摘要
Abstract
According to the low identification of traditional Apriori algorithm for label ranking,this paper proposed an MDLP-Apriori algorithm based discrete Shannon entropy for label ranking.By adding an extra parameter in the Shannon entropy for-mula,and combining with adaptive MDLP algorithm,it increased the ability of Apriori algorithm for recogniting the segmenta-tion point of the lable ranking,which would be more careful observation the label difference.Then,through the experiments on synthetic data set and KEBI test data set with the improved algorithm show that,the MDLP-Apriori algorithm is superior to the contrast algorithm in accuracy and deviation of Kendall coefficient,as well as the number of partitions.Finally,this paper gave the selection criteria of minimum support degree by experiments.关键词
最小描述准则/Apriori 算法/Shannon 熵值/KEBI 数据集/最小支持度Key words
MDLP/Apriori algorithm/Shannon entropy/KEBI data sets/minimum support degree分类
信息技术与安全科学引用本文复制引用
于磊,王普,赵寒,翁壮..基于 MDLP-Apriori 算法的离散 Shannon 熵值标签排序[J].计算机应用研究,2016,33(6):1633-1636,4.
