Descriptive Data Mining (eBook)

David L. Olson, Georg Lauhoff (Autoren)

eBook Download: PDF

2019 | 2nd ed. 2019
XI, 130 Seiten
Springer Singapore (Verlag)
978-981-13-7181-3 (ISBN)

Infos

This book provides an overview of data mining methods demonstrated by software. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools.  Descriptive analytics focus on reports of what has happened.  Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability.  It also includes classification modeling.  Diagnostic analytics can apply analysis to sensor input to direct control systems automatically.  Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems.  Data mining includes descriptive and predictive modeling.  Operations research includes all three.  This book focuses on descriptive analytics.

The book seeks to provide simple explanations and demonstration of some descriptive tools.  This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis.  Chapter 1 gives an overview in the context of knowledge management.  Chapter 2 discusses some basic software support to data visualization.  Chapter 3 covers fundamentals of market basket analysis, and Chapter 4 provides demonstration of RFM modeling, a basic marketing data mining tool.  Chapter 5 demonstrates association rule mining.  Chapter 6 is a more in-depth coverage of cluster analysis. Chapter 7 discusses link analysis.  

Models are demonstrated using business related data.  The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference.  The data sets and software are all selected for widespread availability and access by any reader with computer links.

David L. Olson is the James & H.K. Stuart Professor in MIS and Chancellor's Professor at the University of Nebraska. He has published over 200 articles in refereed journals, primarily on the topic of multiple objective decision-making and information technology. He has authored over 20 books, is co-editor-in-chief of the International Journal of Services Sciences and associate editor of a number of journals. He has given over 150 presentations at international and national conferences. 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, was named the Raymond E. Miles Distinguished Scholar in 2002, and was James C. and Rhonda Seacrest Fellow from 2005 to 2006. He was named Best Enterprise Information Systems Educator by IFIP in 2006. He is a Fellow of the Decision Sciences Institute.

Inhalt

Preface 6
Book Concept 7
Contents 8
About the Authors 10
1 Knowledge Management 11
Computer Support Systems 12
Examples of Knowledge Management 14
Data Mining Descriptive Applications 17
Summary 18
References 18
2 Data Visualization 20
Data Visualization 20
R Software 21
Loan Data 22
Energy Data 29
Basic Visualization of Time Series 30
Conclusion 37
References 39
3 Market Basket Analysis 40
Definitions 41
Co-occurrence 42
Demonstration 46
Fit 47
Profit 47
Lift 50
Market Basket Limitations 52
References 53
4 Recency Frequency and Monetary Analysis 54
Dataset 1 55
Balancing Cells 59
Lift 61
Value Function 62
Data Mining Classification Models 67
Logistic Regression 67
Decision Tree 68
Neural Networks 68
Dataset 2 68
Conclusions 72
References 74
5 Association Rules 76
Methodology 77
The Apriori Algorithm 78
Association Rules from Software 80
Non-negative Matric Factorization 84
Conclusion 85
References 85
6 Cluster Analysis 86
K-Means Clustering 87
A Clustering Algorithm 87
Loan Data 88
Clustering Methods Used in Software 90
Software 91
R (Rattle) K-Means Clustering 91
Other R Clustering Algorithms 97
KNIME 105
WEKA 107
Summary 114
References 115
7 Link Analysis 116
Link Analysis Terms 116
Basic Network Graphics with NodeXL 123
Network Analysis of Facebook Network or Other Networks 127
Link Analysis of Your Emails 133
Link Analysis Application with PolyAnalyst (Olson and Shi 2007) 134
Summary 137
References 137
8 Descriptive Data Mining 138

Produkt-Details

• Erscheint lt. Verlag: 6.5.2019
• Reihe/Serie: Computational Risk Management
• Reihe/Serie: Computational Risk Management
• Zusatzinfo: XI, 130 p. 89 illus., 78 illus. in color.
• Sprache: englisch
• Original-Titel: Descriptive Data Mining
• Themenwelt: Informatik ► Datenbanken ► Data Warehouse / Data Mining
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik
• Themenwelt: Naturwissenschaften
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Allgemeines / Lexika
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
• Schlagworte: Association Rules • cluster analysis • Data Visualization • Descriptive Data Mining • Market basket analysis
• ISBN-10: 981-13-7181-4 / 9811371814
• ISBN-13: 978-981-13-7181-3 / 9789811371813