Data Mining Models, Second Edition (eBook)

Artikelnummer: 978-1-948580-50-2
Einband: Adobe Digital Editions
Verfügbarkeit: Download, sofort verfügbar (Link per E-Mail)
CHF 17.40
decrease increase

Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use.

Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software. The book focuses on demonstrating common business data mining applications. It provides exposure to the data mining process, to include problem identification, data management, and available modeling tools. The book takes the approach of demonstrating typical business data sets with open source software. KNIME is a very easy-to-use tool, and is used as the primary means of demonstration. R is much more powerful and is a commercially viable data mining tool. We also demonstrate WEKA, which is a highly useful academic software, although it is difficult to manipulate test sets and new cases, making it problematic for commercial use.

Schreiben Sie Ihre eigene Bewertung
  • Nur registrierte Benutzer können Produkte bewerten
*
*
Schlecht
Sehr gut
*
*
*
*
VerlagBusiness Expert Press
EinbandAdobe Digital Editions
Erscheinungsjahr2018
Seitenangabe182 S.
AusgabekennzeichenEnglisch
Masse5'978 KB
PlattformEPUB
AutorOlson, David L.

Über den Autor David L. Olson

David L. Olson is the James & H.K. Stuart professor and chancellor's professor at the University of Nebraska. He has published research in over 200 refereed journal articles, primarily on the topic of multiple objective decision-making, information technology, supply chain risk management, and data mining. He has authored over 50 books. He has served as an associate editor of Service Business, Decision Support Systems, Journal of Business Analytics, Decision Sciences and of various IEEE journals. He is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was a Lowry Mays endowed professor at Texas A&M University from 1999 to 2001. He received the Herbert Simon Award for Outstanding Contribution in Information Technology and Decision Making in 2021. He is a fellow of the Decision Sciences Institute.   Desheng Dash Wu is a distinguished professor at the Economics and Management School, University of Chinese Academy of Sciences, Beijing, China. He has published over 150 ISI-indexed papers in refereed journals and 8 books with Springer. His current research interests include mathematical modeling of systems containing uncertain and risky situations, with a special interest in the finance-economics operations interface, maximizing operational and financial goals using the methodologies of game theory and large-scale optimization. He is an elected member of the Academia Europaea (the Academy of Europe), elected member of the European Academy of Sciences and Arts, and elected member of the International Eurasian Academy of Sciences. Cuicui Luo is an associate professor at the International College of the University of Chinese Academy of Sciences, specializing in decision-making, machine learning, risk management, and financial mathematics. She earned her Ph.D. in financial mathematics from the University of Toronto in 2015. She also holds a Master's degree in Mathematical Finance (2009) and a Bachelor's degree in Actuarial Science (2006), both from the University of Toronto. Dr. Luo has published over 30 articles in esteemed academic journals. Her research focuses on the interplay between decision-making, machine learning, and portfolio optimization. She actively contributes to these fields through her scholarly publications and ongoing research. Majid Nabavi earned his B.S. and M.S. degrees from the University of Tehran and MBA and Ph.D. in business administration with an emphasis in operations management from the University of Nebraska-Lincoln. His teaching areas include operations management, management science, database systems, and business analytics. Dr. Nabavi is a faculty member in the College of Business at the University of Nebraska-Lincoln. He has published in Quality Management Journal and Journal of Brand Strategy and presented research in regional and national conferences. He has been a co-principal investigator in research grant proposals and co-authored a book, Introduction to Business Analytics.

Weitere Titel von David L. Olson

Produktbewertungen
Nur registrierte Benutzer können Produkte bewerten