2006•Journal of Wuhan UniversityRequires access

The Dimension Reduction Method Based on Data Transformation

WU Xinling, WU Guo-qing

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Abstract

The data dimension reduction is the main method that can enhance the data mining efficiency based on higher-dimension data set.In this paper,the method and the application using data transformation to reduce data dimension are studied.A general data transformation model is proposed.How to compute the data transformation matrix of the model with the principal component analysis method is introduced explicitly.And a application example about this method is given too.The example indicates that (it's) possible to capture the maximum variations of the original variables with fewer variables.

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What this paper is about

The data dimension reduction is the main method that can enhance the data mining efficiency based on higher-dimension data set.In this paper,the method and the application using data transformation to reduce data dimension are studied.A general data transformation model is proposed.How to compute the data transformation matrix of the model with the principal component analysis method is introduced explicitly.And a application example about this method is given too.The example indicates that (it's) possible to capture the maximum variations of the original variables with fewer variables.

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Available abstract

The data dimension reduction is the main method that can enhance the data mining efficiency based on higher-dimension data set.In this paper,the method and the application using data transformation to reduce data dimension are studied.A general data transformation model is proposed.How to compute the data transformation matrix of the model with the principal component analysis method is introduced explicitly.And a application example about this method is given too.The example indicates that (it's) possible to capture the maximum variations of the original variables with fewer variables.

Key concepts: Dimension (graph theory), Dimensionality reduction, Transformation (genetics), Principal component analysis, Reduction (mathematics), Data transformation, Data set, Computer science

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