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 :
بازگشت