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Principles and theories of data mini...

Ramjan, Sarawut, (1984-)

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Principles and theories of data mining with RapidMiner

Record Type:	Electronic resources : Monograph/item
Title/Author:	Principles and theories of data mining with RapidMiner/ authored by Sarawut Ramjan.
Author:	Ramjan, Sarawut,
Published:	Hershey, Pennsylvania :IGI Global, : 2023.,
Description:	1 online resource (vii, 319 p.) :ill.
[NT 15003449]:	Chapter 1. Introduction to data mining -- Chapter 2. Data -- Chapter 3. Software installation and introduction to RapidMiner -- Chapter 4. Data pre-processing and example of data classification with RapidMiner -- Chapter 5. Classification -- Chapter 6. Deep learning -- Chapter 7. Clustering -- Chapter 8. Association rule -- Chapter 9. Recommendation system -- Chapter 10. Case studies on the use of data mining techniques in data science -- Chapter 11. Data mining for junior data scientists: basic Python programming -- Chapter 12. Data mining for junior data scientists: data analytics with Python.
Subject:	Data mining. -
Online resource:	https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-4730-7
ISBN:	9781668447321

Principles and theories of data mining with RapidMiner
Ramjan, Sarawut,1984-

Principles and theories of data mining with RapidMiner[electronic resource] /authored by Sarawut Ramjan. - Hershey, Pennsylvania :IGI Global,2023. - 1 online resource (vii, 319 p.) :ill.

Includes bibliographical references and index.

Chapter 1. Introduction to data mining -- Chapter 2. Data -- Chapter 3. Software installation and introduction to RapidMiner -- Chapter 4. Data pre-processing and example of data classification with RapidMiner -- Chapter 5. Classification -- Chapter 6. Deep learning -- Chapter 7. Clustering -- Chapter 8. Association rule -- Chapter 9. Recommendation system -- Chapter 10. Case studies on the use of data mining techniques in data science -- Chapter 11. Data mining for junior data scientists: basic Python programming -- Chapter 12. Data mining for junior data scientists: data analytics with Python.

"This book is academically written as a guide for students and people interested in experimenting Data Mining using RapidMiner software. It covers the contents related to Data Mining, which consists of Classification, Deep Learning,Association Rule, Clustering, Recommendation System and RapidMiner Software usage as well as researching case studies on the use of data mining techniques in data science. Additionally, this book is the foundation of Python programming fordata science for young scientists who want to understand data mining algorithms. As well as starting to write programs that can be applied to other data science programs. At the end of this book, authors describe about data governance witha case study of the government sector to enable young data scientists to understand the role of data scientists as part of stakeholders in data governance actions. The authors hope that this book is a good beginning for those who would like to develop themselves or for those who own data within their organization to meet internal and external problems. RapidMiner software is used to analyze data and provide guidance for further study in data science at a higher level"--

Mode of access: World Wide Web.

ISBN: 9781668447321Subjects--Uniform Titles:

RapidMiner (Electronic resource)
Subjects--Topical Terms:

562972
Data mining.
Subjects--Index Terms:

Association Rule.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QA76.9.D343 / R356 2023eb

Dewey Class. No.: 006.3/12

Principles and theories of data mining with RapidMiner
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245 1 0 $a Principles and theories of data mining with RapidMiner $h [electronic resource] / $c authored by Sarawut Ramjan.
260 $a Hershey, Pennsylvania : $b IGI Global, $c 2023.
300 $a 1 online resource (vii, 319 p.) : $b ill.
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. Introduction to data mining -- Chapter 2. Data -- Chapter 3. Software installation and introduction to RapidMiner -- Chapter 4. Data pre-processing and example of data classification with RapidMiner -- Chapter 5. Classification -- Chapter 6. Deep learning -- Chapter 7. Clustering -- Chapter 8. Association rule -- Chapter 9. Recommendation system -- Chapter 10. Case studies on the use of data mining techniques in data science -- Chapter 11. Data mining for junior data scientists: basic Python programming -- Chapter 12. Data mining for junior data scientists: data analytics with Python.
520 $a "This book is academically written as a guide for students and people interested in experimenting Data Mining using RapidMiner software. It covers the contents related to Data Mining, which consists of Classification, Deep Learning,Association Rule, Clustering, Recommendation System and RapidMiner Software usage as well as researching case studies on the use of data mining techniques in data science. Additionally, this book is the foundation of Python programming fordata science for young scientists who want to understand data mining algorithms. As well as starting to write programs that can be applied to other data science programs. At the end of this book, authors describe about data governance witha case study of the government sector to enable young data scientists to understand the role of data scientists as part of stakeholders in data governance actions. The authors hope that this book is a good beginning for those who would like to develop themselves or for those who own data within their organization to meet internal and external problems. RapidMiner software is used to analyze data and provide guidance for further study in data science at a higher level"-- $c Provided by publisher.
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650 0 $a Data mining. $3 562972
650 0 $a Big data. $3 2045508
653 $a Association Rule.
653 $a Case Study.
653 $a Classification.
653 $a Clustering.
653 $a Data.
653 $a Data Pre-Processing and Example of Data Classification With RapidMiner.
653 $a Deep Learning.
653 $a Introduction to Data Mining.
653 $a Recommendation System.
653 $a Software Installation and Introduction to RapidMiner.
655 4 $a Electronic books. $2 lcsh $3 542853
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