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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.

Application of data mining techniques to find relationships between the dishes offered by a restaurant for the elaboration of combos based on the preferences of the diners

Vazquez Rosa Maria, Bonilla Edmundo, Sanchez Eduardo, Atriano Oscar, Berruecos Cinthya

Applied Computer Science

2019

Vol. 15, no 2

73--88

Currently, blended food has been a common menu item in fast food restaurants. The sales of the fast-food industry grow thanks to several sales strategies, including the “combos”, so, specialty, regional, family and buffet restaurants are even joining combos’ promotions. This research paper presents the implementation of a system that will serve as support to elaborate combos according to the preferences of the diners using data mining techniques to find relationships between the different dishes that are offered in a restaurant. The software resulting from this research is being used by the mobile application Food Express, with which it communicates through webservices. References