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Tests on categorical data -- Ch. 16.
In this paper, we present a method for performing multidimensional clustering on categorical data and show its superiority over unidimensional clustering.
Methods based on remote sensing (RS) features, such as vegetation indices, have been proposed as promising approaches, complementing methods based on categorical data to support decision making.
In addition, the inclusion of fuzzy arguments, against which data values can be compared, greatly reduces the potential for combinatorial explosion that often occurs in expert systems that rely on categorical data interpretation, while at the same time providing a robust description of complex systems.
We base the simulations on categorical data from the American Community Survey.
It is therefore not obvious to compare the results produced by the PMR-Transitive, which operates on categorical data (records), with such methods.
Similar(37)
Contingency tables of categorical data on litter sizes and pup weight at weaning (3 × 4 and 2 × 3 tables, respectively) were analyzed by χ-test.
In a study on the imputation of categorical data [ 29] it was found that, while problems exist when imputing using a variant of NORM designed to deal with categorical data when many variables are present, the same limitations are not problematic for NORM.
Comparisons of AKI patients according to their maximum RIFLE class were based on χ2 tests for categorical data and on one-way analysis of variance or the Kruskal-Wallis test for continuous data as appropriate.
Comparisons of patients with and those without AKI were based on χ2 tests for categorical data and on Student's t-test or Wilcoxon's rank-sum test for continuous data as appropriate.
In the whole cohort, comparisons of patients with and those without RRT were based on chi-square tests for categorical data, and on Student's t-test or Wilcoxon's test for continuous data, as appropriate.
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