High Dimensional and Large Numbers of Data Clustering Method Based Sensitive Subspace
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@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}
}
TY - JOUR
T1 - High Dimensional and Large Numbers of Data Clustering Method Based Sensitive Subspace
AU -
JO - Journal of Information and Computing Science
VL - 3
SP - 197
EP - 202
PY - 2024
DA - 2024/01
SN - 2
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22798.html
KW - Data clustering, Sensitive subspace, Probability density, High dimensional data, Large numbers
of data, hyperspectral data
AB - 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.
. (2024). High Dimensional and Large Numbers of Data Clustering Method Based Sensitive Subspace.
Journal of Information and Computing Science. 2 (3).
197-202.
doi:
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