Evaluation of Orange data mining software and examples for lecturing machine learning tasks in geoinformatics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F24%3A73625059" target="_blank" >RIV/61989592:15310/24:73625059 - isvavai.cz</a>

Result on the web

<a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.22735" target="_blank" >https://onlinelibrary.wiley.com/doi/epdf/10.1002/cae.22735</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cae.22735" target="_blank" >10.1002/cae.22735</a>

Result language

angličtina

Original language name

Evaluation of Orange data mining software and examples for lecturing machine learning tasks in geoinformatics

Original language description

The article presents the advantages of, and possible uses for, Orange software for data mining in combina-tion with processing spatial data by ArcGIS Pro software in education. To present suitability of Orange software in education, the scientific method of Physics of Notation by D. Moody is used to evaluate the Or-ange software&apos;s visual vocabulary. All nine principles are applied in the presented evaluation. As a result, a high level of effective cognition of the Orange visual vocabulary is proven by this method. Namely, the Se-mantic Transparency of visual vocabulary, thanks the explicit inner icons, is semantically immediate. Also, Principle of Dual Coding is used properly by automatic text labels of graphical symbols with the oppor-tunity to rename labels. Renaming is also a way to ensure the partial overloading of symbols found by the first Principle of Semiotic Clarity. The Principle of Cognitive Interaction is partially fulfilled by automati-cally reorganising connector lines between symbols to reduce the crossing of lines. A high level of effective cognition is beneficial for students. The evaluation of the visual notation of Orange software is presented to inform teachers and the geoinformatics community of the highly effective cognitive aspects of Orange soft-ware. The two practical lectures of processing in Orange and ArcGIS Pro software are shown to the teachers and students of geoinformatics community as examples of machine learning tasks. They are cluster anal-yses carried out with the DBSCAN method, first for the location of cafés in Olomouc town, and the second example concerns finding similar European towns based on their land use arrangement, using the neural network and following hierarchical clustering. Both examples could provide inspiration for the geoinfor-matics community to adopt Orange data mining software.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION

ISSN

1061-3773

e-ISSN

1099-0542

Volume of the periodical

32

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

1-18

UT code for WoS article

001187621600001

EID of the result in the Scopus database

2-s2.0-85188610811