DocumentCode
2457643
Title
The Approach of Adaptive Spectral Clustering Analyze on High Dimensional Data
Author
Cai, Liping ; Zhou, Xuchuan ; Song, Jancheng
Author_Institution
Coll. of Comput. Sci. & Technol., Southwest Univ. for Nat., Chengdu, China
fYear
2010
fDate
17-19 Dec. 2010
Firstpage
160
Lastpage
162
Abstract
Data mining is an important tool in knowledge discovery. It aims at discovering the valuable patterns hidden in large volume of data. In a distributed environment, the main problem we need to consider for data mining is how to transfer the minimal amount of data and provide maximum sharing of information with the continued expansion of business scale and constant update of services content. For high-dimensional scientific data, an adaptive spectral clustering method has been proposed. The experimental results on numerical simulation of scientific data have shown the improvement of proposed method.
Keywords
data mining; pattern clustering; adaptive spectral clustering; data mining; high dimensional scientific data; information sharing; knowledge discovery; numerical simulation; Algorithm design and analysis; Clustering algorithms; Data mining; Data models; Nearest neighbor searches; Principal component analysis; Simulation; High Dimensional Data; Projection; Spectral Clustering; Subspace;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational and Information Sciences (ICCIS), 2010 International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-8814-8
Electronic_ISBN
978-0-7695-4270-6
Type
conf
DOI
10.1109/ICCIS.2010.45
Filename
5709038
Link To Document