Paper Info
Title
A Shrinking-Based Dimension Reduction Approach for Multi-Dimensional Data Analysis
Abstract
In this paper, we present continuous research on dataanalysis based on our previous work on the shrinking approach.Shrinking[A shrinking-based approach for multi-dimensional data analysis] is a novel data preprocessing technique which optimizes the inner structure of data inspiredby the Newton's Universal Law of Gravitation[The laws of physics] in the realworld. It can be applied in many data mining fields. Followingour previous work on the shrinking method for multi-dimensionaldata analysis in full data space, we propose ashrinking-based dimension reduction approach which tendsto solve the dimension reduction problem from a new perspective.In this approach data are moved along the directionof the density gradient, thus making the inner structureof data more prominent. It is conducted on a sequence ofgrids with different cell sizes. Dimension reduction processis performed based on the difference of the data distributionprojected on each dimension before and after the data-shrinkingprocess. Those dimensions with dramatic variationof data distribution through the data-shrinking processare selected as good dimension candidates for furtherdata analysis. This approach can assist to improve the performanceof existing data analysis approaches. We demonstratehow this shrinking-based dimension reduction approachaffects the clustering results of well known algorithms.
Year
DOI
Venue
2004
10.1109/SSDBM.2004.8
SSDBM
Keywords
DocType
ISBN
dimension reduction problem,data mining field,Multi-Dimensional Data Analysis,novel data,inner structureof data,Shrinking-Based Dimension Reduction Approach,ashrinking-based dimension reduction approach,dramatic variationof data distribution,performanceof existing data analysis,full data space,approach data,multi-dimensional data analysis
Conference
0-7695-2146-0
Citations 
PageRank 
References 
3
0.42
0
Authors
2
Name
Order
Citations
PageRank
Yong Shi174.91
Aidong Zhang22970405.63