A Data Preprocessing Algorithm For Classification Model Based On Rough Sets

摘要：

aimed to solve the limitation of abundant data to constructing classification modeling in data mining, the paper proposed a novel effective preprocessing algorithm based on rough sets. Firstly, we construct the relation Information System using original data sets. Secondly, make use of attribute reduction theory of Rough sets to produce the Core of Information System. Core is the most important and necessary information which cannot reduce in original Information System. So it can get a same effect as original data sets to data analysis, and can construct classification modeling using it. Thirdly, construct indiscemibility matrix using reduced Information System, and finally, get the classification of original data sets. Compared to existing techniques, the developed algorithm enjoy following advantages: (1) avoiding the abundant data in follow-up data processing, and (2) avoiding large amount of computation in whole data mining process. (3) The results become more effective because of introducing the attribu tes reducing theory of Rough Sets.

关键词： rough sets classification data mining

作者: Li Xiang-wei Qi Yian-fang

作者单位: Department of Computer Engineering, Lanzhou Polytechnic College Lanzhou, China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou, China

会议类型: 国际会议

会议名称: 2010 International Conference on Circuit and Signal Processing(2010年电路与信号处理国际会议 ICCSP 2010)

会议地点: 上海

会议语种:英文

页码: 113-115

在线出版日期: 2010-12-25（万方平台首次上网日期，不代表论文的发表时间）