Hierarchical Classification Model of Attribute Decomposition Approach Based on Rough Set
In data mining,it is difficult to construct classification model for massive high-dimension databases.Time complexity of computation is high,and obtained classification models are difficult to understand or interpret.Based on rough set theory,this paper proposed a new attribute decomposition approach to discover concept hierarchy in the database and establish hierarchical cIassification models.For familiar databases with prior knowledge,several attributes are grouped together;for unfamiliar databases,attributes are selected and grouped together according to data table decomposition measure;and then objects’classes are re-labeled according to the coincidence search indicator proposed iU this paper.Then discover intermediate concept layer,construct hierarchical classification models,divide massive high-dimension databasesinto small databases hierarchicallv.Besides,because intermediate concept layers have certain physical meaning,theunderstandability of the model is greatly improved.Finally,this paper validated the effectiveness of this algorithm with cases andpublic datasets from UCI.The result shows that this algorithm can produce hierarchical classification models with clear hierarchies,strong understandability,while still keeping high classification rate.
Qizhong Zhang
国际会议
The International Conference Information Computing and Automation(2007国际信息计算与自动化会议)
成都
英文
121-126
2007-12-19（万方平台首次上网日期，不代表论文的发表时间）