A NEW APPROACH TO HIERARCHICAL CLUSTERING USING PARTIAL LEAST SQUARES
We here propose a methodology to improve Hierarchical Cluster Analysis using Partial Least Squares (PLS). Two problems are addressed by this methodology, these are (1)when, as usually, Euclidean distance is used for Hierarchical Cluster Analysis, but Euclidean distance is defined only in Euclidean space. If Euclidean distances are computed in other spaces, the distances are hard to make sense. On the other hand, since the variables in the data set do not have equal variance, they do not have comparable scales. (2) Traditional clustering methods are based on single data table, but the application of PLS makes it possible to deal with multiply data tables problems. In addition, the proposed method can reduce the dimension of classification variables in a reasonable way.That makes it possible to demonstrate the relationship of multiply dimension data.
Partial Least Squares (PLS) Hierarchical Cluster Analyses Euclidean space Euclidean distance
JIN-LAN LIU YIN BAI JIAN KANG NA AN
School of Management, Tianjin University, Tianjin 300072,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
1125-1131
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）