High Dimensional and Large Numbers of Data Clustering Method Based Sensitive Subspace
Year: 2007
Journal of Information and Computing Science, Vol. 2 (2007), Iss. 3 : pp. 197–202
Abstract
Clustering is the main method to analyse the large numbers of data, but when the data’s dimension is higher, the consumed time increases exponentially. We put forward an effective clustering method for high dimensional and large numbers of data, which is based on the sensitive subspace consisting of the data set’s sensitive dimensions. In order to build the sensitive subspace, we first estimate the probability density of each dimension, enhance its optional ability through extracting zero and smoothness processing, then through recognizing the number of the rallying points to gain the sensitive dimensions, and last do the Rival Penalized Competitive Learning (RPCL) clustering in the subspace. Moreover, we detected the red tide of hyper-spectral data using this method, which proved it could effectively get similar results with one-ninth time.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2024-JICS-22798
Journal of Information and Computing Science, Vol. 2 (2007), Iss. 3 : pp. 197–202
Published online: 2007-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 6