Distributed dimensionality reduction of industrial
data based on clustering
Zhang, Yongyan, Xie, Guo, Wang, Wenqing, Qian, Fucai, Du, Xulong and Du, JinhuaORCID: 0000-0002-3267-4881
(2018)
Distributed dimensionality reduction of industrial
data based on clustering.
In: 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 1 May – 2 June 2018, Wuhan, China.
ISBN 978-1-5386-3758-6
Large amounts of data are produced in system
operation, and how to extract effective information from these
data has become an important research topic in the industrial
application. Dimensionality reduction is a way to refine the data.
Because of the low efficiency of the existing methods, these
methods can’t discover the internal structure of the data.
Regarding these problems, a distributed method of
dimensionality reduction based on clustering is proposed, which
includes the following steps:(1) Clustering the data into some
small classes according to the similarity between the data
variables; (2) reducing the dimension of data in a small class
after being clustered respectively; (3) merging the data after
being reduced dimension; (4) classifying the data after being
merged by support vector machine (SVM). The data in the
simulation is the test data, and the results show that the methods
proposed in this paper are better than the existing dimensionality
reduction methods.
This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:
National Natural Science Foundation of China (No.U1534208㸪No.61773016, and No. 61703334) and Science and technology plan of Shaanxi Province (No. 2016KJXX-79ˈand S2015YFJM0027).
ID Code:
23336
Deposited On:
21 May 2019 15:45 by
Thomas Murtagh
. Last Modified 21 May 2019 15:45