INTERVAL-VALUED EXAMPLES LEARNING BASED ON FUZZY C-MEAN CLUSTERING
In this paper, a new approach to generate decision tree from those examples with interval-valued attributes is presented and then rule matching is made. Considering that the interval values of the same attribute of all examples probably fall into certain distributing rule so as to form some center points, we cluster the interval-valued attributes of all examples by using the algorithm of FCCID (Fuzzy C-mean Clustering for Interval-valued Data). Consequently, the attributes represented by interval data are transformed into those represented by fuzzy degree of membership. On the basis of that, the fuzzy ID3 algorithm is adopted to generate a decision tree for rule matching.
Interval-valued data fuzzy clustering learning from examples fuzzy ID3
MING-ZHI CHEN GUO-LONG CHEN SHUI-LI CHEN
College of Math.& Computer Science, Fuzhou University, Fuzhou, 350002, China School of Science, Jimei University, Xiamen, Fujian 361021, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
1153-1158
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）