@Article{JICS-2-197,
author = {},
title = {High Dimensional and Large Numbers of Data Clustering Method Based Sensitive Subspace},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {2},
number = {3},
pages = {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. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22798.html}
}