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Alternatives for Greedy Discrete Subsampling: Various Approaches Including Cluster Subsampling of COVID-19 Data With No Response Variable

Štěpánek Lubomír, Habarta Filip, Malá Ivana, Marek Luboš

Annals of Computer Science and Information Systems

2021

Vol. 26

103--111

An exhaustive selection of all possible combinations of n = 400 from N = 698 observations of the COVID-19 dataset was used as a benchmark. Building a random set of subsamples and choosing the one that minimized an averaged sum of squares of each variable's category frequency returned similar results as a "forward" subselection reducing the dataset one-by-one observation by the same metric's permanent lowering. That works similarly as k-means clustering (with a random clusters' number) over the original dataset's observations and choosing a subsample from each cluster proportionally to its size. However, the approaches differ significantly in asymptotic time complexity.

Weryfikacja po płońsku

Królikowski J.

Geodeta : magazyn geoinformacyjny

2016

nr 4

34--35