Hyper-Spectral Data Clustering Method Based Upon The Sensitive Subspace
We put forward a quick clustering method for large numbers of data, with high dimensions, which is based on sensitive subspace consisting of the data set's sensitive dimensions. We first estimate the probability density of each dimension by the parzen window algorithm, enhance its optional ability through extracting zeroes and smoothness processing, then through the recognition of the number of the rallying points and the gain of the sensitive dimensions in order to compose the sensitive subspace, and lastly, we perform the Rival Penalized Competitive Learning (RPCL) clustering in the subspace. Moreover, we detected the red tide of hyper-spectral data using this method. Furthermore, the overall improvement in terms of the computational speed is about nine times faster.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 03 (2007)
Issue (Month): 02 ()
|Contact details of provider:|| Web page: http://www.worldscinet.com/nmnc/nmnc.shtml|
|Order Information:|| Email: |
When requesting a correction, please mention this item's handle: RePEc:wsi:nmncxx:v:03:y:2007:i:02:p:271-280. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tai Tone Lim)
If references are entirely missing, you can add them using this form.