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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

COMPUTER APPLICATIONS IN ENGINEERING EDUCATION

ISSN

1061-3773

e-ISSN

1099-0542

Svazek periodika

32

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

1-18

Kód UT WoS článku

001187621600001

EID výsledku v databázi Scopus

2-s2.0-85188610811