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Informationen zum Autor Andrea Ahlemeyer-Stubbe , Director Strategic Analytics, DRAFTFCB München GmbH, Germany Shirley Coleman , Principal Statistician, Industrial Statistics Research Unit, School of Maths and Statistics, Newcastle University, UK Klappentext Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest. Zusammenfassung Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest. Inhaltsverzeichnis Glossary of terms xii Part I Data Mining Concept 1 1 Introduction 3 1.1 Aims of the Book 3 1.2 Data Mining Context 5 1.2.1 Domain Knowledge 6 1.2.2 Words to Remember 7 1.2.3 Associated Concepts 7 1.3 Global Appeal 8 1.4 Example Datasets Used in This Book 8 1.5 Recipe Structure 11 1.6 Further Reading and Resources 13 2 Data Mining Definition 14 2.1 Types of Data Mining Questions 15 2.1.1 Population and Sample 15 2.1.2 Data Preparation 16 2.1.3 Supervised and Unsupervised Methods 16 2.1.4 Knowledge-Discovery Techniques 18 2.2 Data Mining Process 19 2.3 Business Task: Clarification of the Business Question behind the Problem 20 2.4 Data: Provision and Processing of the Required Data 21 2.4.1 Fixing the Analysis Period 22 2.4.2 Basic Unit of Interest 23 2.4.3 Target Variables 24 2.4.4 Input Variables/Explanatory Variables 24 2.5 Modelling: Analysis of the Data 25 2.6 Evaluation and Validation during the Analysis Stage 25 2.7 Application of Data Mining Results and Learning from the Experience 28 Part II Data Mining Practicalities 31 3 All about data 33 3.1 Some Basics 34 3.1.1 Data, Information, Knowledge and Wisdom 35 3.1.2 Sources and Quality of Data 36 3.1.3 Measurement Level and Types of Data 37 3.1.4 Measures of Magnitude and Dispersion 39 3.1.5 Data Distributions 41 3.2 Data Partition: Random Samples for Training, Testing and Validation 41 3.3 Types of Business Information Systems 44 3.3.1 Operational Systems Supporting Business Processes 44 3.3.2 Analysis-Based Information Systems 45 3.3.3 Importance of Information 45 3.4 Data Warehouses 47 3.4.1 Topic Orientation 47 3.4.2 Logical Integration and Homogenisation 48 3.4.3 Reference Period 48 3.4.4 Low Volatility 48 3.4.5 Using the Data Warehouse 49 3.5 Three Components of a Data Warehouse: DBMS, DB and DBCS 50 3.5.1 Database Management System (DBMS) 51 3.5.2 Database (DB) 51 3.5.3 Database Communication Systems (DBCS) 51 3.6 Data Marts 52 3.6.1 Regularly Filled Data Marts 53 3.6.2 Comparison between Data Marts and Data Wareh...
