Extracting Information from Interval Data Using Symbolic Principal Component Analysis
2017 ◽
Vol 46
(3-4)
◽
pp. 79-87
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Keyword(s):
We introduce generic definitions of symbolic variance and covariance for random interval-valued variables, that lead to a unified and insightful interpretation of four known symbolic principal component estimation methods: CPCA, VPCA, CIPCA, and SymCovPCA. Moreover, we propose the use of truncated versions of symbolic principal components, that use a strict subset of the original symbolic variables, as a way to improve the interpretation of symbolic principal components. Furthermore, the analysis of a real dataset leads to a meaningful characterization of Internet traffic applications, while highligting similarities between the symbolic principal component estimation methods considered in the paper.
2017 ◽
Vol 921
(3)
◽
pp. 24-29
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1989 ◽
Vol 20
(1)
◽
pp. 26-27
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2014 ◽
Vol 926-930
◽
pp. 4085-4088
2002 ◽
Vol 33
(12)
◽
pp. 924-931
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2013 ◽
Vol 834-836
◽
pp. 935-938
2015 ◽
Vol 50
(8)
◽
pp. 649-657
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2020 ◽
2022 ◽