NDLI: A New Approach to Hierarchical Clustering Using Partial Least Squares

Content Provider	IEEE Xplore Digital Library
Author	Jin-Lan Liu Yin Bai Jian Kang Na An
Copyright Year	2006
Description	Author affiliation: Sch. of Manage., Tianjin Univ. (Jin-Lan Liu; Yin Bai; Jian Kang; Na An)
Abstract	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
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	1125
Ending Page	1131
File Size	215400
Page Count	7
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258591
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares methods Euclidean distance Clustering methods Linear regression Distortion measurement Vectors Data analysis Extraterrestrial measurements Statistics Independent component analysis Partial Least Squares (PLS) Hierarchical Cluster Analyses Euclidean space
Content Type	Text
Resource Type	Article

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