Visual tools to lecture data analytics and engineering

Sung Bae Cho, Antonio J. Tallón-Ballesteros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper analyses some tools that could be appropriate as teaching resources for undergraduate or postgraduate levels. A comparison is performed between two machine learning tools such as Weka and RapidMiner on one side, and with Minitab, on the other side, that is a more statistical tool and also covers some parts of the Cross Industry Standard Process for Data Mining. We describe the functionalities of those frameworks and also the installation and running procedure. A road-map is carried out in order to state the main tasks that are available in these tools and to encourage other researchers or lecturers to introduce them in laboratory classes.

Original languageEnglish
Title of host publicationNatural and Artificial Computation for Biomedicine and Neuroscience - International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Proceedings
PublisherSpringer Verlag
Pages551-558
Number of pages8
Volume10338 LNCS
ISBN (Print)9783319597720
DOIs
Publication statusPublished - 2017 Jan 1
Event7th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017 - Corunna, Spain
Duration: 2017 Jun 192017 Jun 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10338 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017
CountrySpain
CityCorunna
Period17/6/1917/6/23

Fingerprint

Engineering
Data mining
Learning systems
Data Mining
Machine Learning
Teaching
Cover
Industry
Resources
Vision
Class
Standards
Framework

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Cho, S. B., & Tallón-Ballesteros, A. J. (2017). Visual tools to lecture data analytics and engineering. In Natural and Artificial Computation for Biomedicine and Neuroscience - International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Proceedings (Vol. 10338 LNCS, pp. 551-558). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10338 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59773-7_56
Cho, Sung Bae ; Tallón-Ballesteros, Antonio J. / Visual tools to lecture data analytics and engineering. Natural and Artificial Computation for Biomedicine and Neuroscience - International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Proceedings. Vol. 10338 LNCS Springer Verlag, 2017. pp. 551-558 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{8e01b79e147b46e090fe5314707149bc,
title = "Visual tools to lecture data analytics and engineering",
abstract = "This paper analyses some tools that could be appropriate as teaching resources for undergraduate or postgraduate levels. A comparison is performed between two machine learning tools such as Weka and RapidMiner on one side, and with Minitab, on the other side, that is a more statistical tool and also covers some parts of the Cross Industry Standard Process for Data Mining. We describe the functionalities of those frameworks and also the installation and running procedure. A road-map is carried out in order to state the main tasks that are available in these tools and to encourage other researchers or lecturers to introduce them in laboratory classes.",
author = "Cho, {Sung Bae} and Tall{\'o}n-Ballesteros, {Antonio J.}",
year = "2017",
month = "1",
day = "1",
doi = "10.1007/978-3-319-59773-7_56",
language = "English",
isbn = "9783319597720",
volume = "10338 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "551--558",
booktitle = "Natural and Artificial Computation for Biomedicine and Neuroscience - International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Proceedings",
address = "Germany",

}

Cho, SB & Tallón-Ballesteros, AJ 2017, Visual tools to lecture data analytics and engineering. in Natural and Artificial Computation for Biomedicine and Neuroscience - International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Proceedings. vol. 10338 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10338 LNCS, Springer Verlag, pp. 551-558, 7th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, 17/6/19. https://doi.org/10.1007/978-3-319-59773-7_56

Visual tools to lecture data analytics and engineering. / Cho, Sung Bae; Tallón-Ballesteros, Antonio J.

Natural and Artificial Computation for Biomedicine and Neuroscience - International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Proceedings. Vol. 10338 LNCS Springer Verlag, 2017. p. 551-558 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10338 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Visual tools to lecture data analytics and engineering

AU - Cho, Sung Bae

AU - Tallón-Ballesteros, Antonio J.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - This paper analyses some tools that could be appropriate as teaching resources for undergraduate or postgraduate levels. A comparison is performed between two machine learning tools such as Weka and RapidMiner on one side, and with Minitab, on the other side, that is a more statistical tool and also covers some parts of the Cross Industry Standard Process for Data Mining. We describe the functionalities of those frameworks and also the installation and running procedure. A road-map is carried out in order to state the main tasks that are available in these tools and to encourage other researchers or lecturers to introduce them in laboratory classes.

AB - This paper analyses some tools that could be appropriate as teaching resources for undergraduate or postgraduate levels. A comparison is performed between two machine learning tools such as Weka and RapidMiner on one side, and with Minitab, on the other side, that is a more statistical tool and also covers some parts of the Cross Industry Standard Process for Data Mining. We describe the functionalities of those frameworks and also the installation and running procedure. A road-map is carried out in order to state the main tasks that are available in these tools and to encourage other researchers or lecturers to introduce them in laboratory classes.

UR - http://www.scopus.com/inward/record.url?scp=85027177752&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027177752&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-59773-7_56

DO - 10.1007/978-3-319-59773-7_56

M3 - Conference contribution

SN - 9783319597720

VL - 10338 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 551

EP - 558

BT - Natural and Artificial Computation for Biomedicine and Neuroscience - International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Proceedings

PB - Springer Verlag

ER -

Cho SB, Tallón-Ballesteros AJ. Visual tools to lecture data analytics and engineering. In Natural and Artificial Computation for Biomedicine and Neuroscience - International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Proceedings. Vol. 10338 LNCS. Springer Verlag. 2017. p. 551-558. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-59773-7_56