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Identifying a Correlation among Qualitative Non-Numeric Parameters in Natural Fish Microbe Dataset Using Machine Learning.

Identifying a Correlation among Qualitative Non-Numeric Parameters in Natural Fish Microbe Dataset Using Machine Learning.

Authors :
Shima, Hideaki
Sato, Yuho
Sakata, Kenji
Asakura, Taiga
Kikuchi, Jun
Source :
Applied Sciences (2076-3417); Jun2022, Vol. 12 Issue 12, p5927, 16p
Publication Year :
2022

Abstract

Recent technical innovations and developments in computer-based technology have enabled bioscience researchers to acquire comprehensive datasets and identify unique parameters within experimental datasets. However, field researchers may face the challenge that datasets exhibit few associations among any measurement results (e.g., from analytical instruments, phenotype observations as well as field environmental data), and may contain non-numerical, qualitative parameters, which make statistical analyses difficult. Here, we propose an advanced analysis scheme that combines two machine learning steps to mine association rules between non-numerical parameters. The aim of this analysis is to identify relationships between variables and enable the visualization of association rules from data of samples collected in the field, which have less correlations between genetic, physical, and non-numerical qualitative parameters. The analysis scheme presented here may increase the potential to identify important characteristics of big datasets. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
12
Issue :
12
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
157639732
Full Text :
https://doi.org/10.3390/app12125927