Extracting Information from Interval Data Using Symbolic Principal Component Analysis
نویسندگان
چکیده
We address the definition of symbolic variance and covariance for random interval-valued variables, and present four known symbolic principal component estimation methods using a common insightful framework. In addition, we provide a simple explicit formula for the scores of the symbolic principal components, equivalent to the representation by Maximum Covering Area Rectangle. Furthermore, the analysis of a real dataset leads to a meaningful characterization of Internet traffic applications.
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تاریخ انتشار 2016