Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
Pattern Recognition Letters
Pattern Recognition Letters
Dynamic clustering for interval data based on L2 distance
Computational Statistics
Symbolic Data Analysis and the SODAS Software
Symbolic Data Analysis and the SODAS Software
Dynamic clustering of interval data using a Wasserstein-based distance
Pattern Recognition Letters
Information Sciences: an International Journal
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In many statistical analysis methods, standardization of the sample data is usually recommended to prevent the results from being strongly affected by the scale of measurement of the variables. This paper focuses on the standardization of interval data obtained by symbolic data analysis (SDA). SDA is a new data analysis technique which captures the value of a variable with a symbolic representation. The empirical descriptive statistics of the interval symbolic variable are studied first. We then proposed the standardization method of interval symbolic data and conducted a simulation study to evaluate our standardization method by using clustering analysis. An application example on e-shops of several major cities in China is given at the end of the paper. Differing from previous research, we do not require the assumption of uniformly distributed data in the interval. Our method makes the best use of the original sample information.