Data Mining for Business Analytics

Galit Shmueli , Peter C. Bruce , Nitin R. Patel 지음

Wiley 2016년 04월 22일 출간

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Praise for the Second Edition "…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing." Research Magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." ComputingReviews.com "Excellent choice for business analysts...The book is a perfect fit for its intended audience." Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization "…extremely well organized, clearly written and introduces all of the basic ideas quite well." Robert L. Phillips, Professor of Professional Practice, Columbia Business School Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner？, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft？ Office Excel？ add-in XLMiner？ to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on soc

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Foreword xvii Preface to the Third Edition xix Preface to the First Edition xxii Acknowledgments xxiv PART I PRELIMINARIES CHAPTER 1 Introduction 3 1.1 What is Business Analytics? 3 1.2 What is Data Mining? 5 1.3 Data Mining and Related Terms 5 1.4 Big Data 6 1.5 Data Science 7 1.6 Why Are There So Many Different Methods? 8 1.7 Terminology and Notation 9 1.8 Road Maps to This Book 11 Order of Topics 12 CHAPTER 2 Overview of the Data Mining Process 14 2.1 Introduction 14 2.2 Core Ideas in Data Mining 15 2.3 The Steps in Data Mining 18 2.4 Preliminary Steps 20 2.5 Predictive Power and Overfitting 26 2.6 Building a Predictive Model with XLMiner 30 2.7 Using Excel for Data Mining 40 2.8 Automating Data Mining Solutions 40 Data Mining Software Tools (by Herb Edelstein) 42 Problems 45 PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization 50 3.1 Uses of Data Visualization 50 3.2 Data Examples 52 Example 1: Boston Housing Data 52 Example 2: Ridership on Amtrak Trains 53 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 53 Distribution Plots 54 Heatmaps: Visualizing Correlations and Missing Values 57 3.4 Multi-Dimensional Visualization 58 Adding Variables 59 Manipulations 61 Reference: trend line and labels 64 Scaling up to Large Datasets 65 Multivariate Plot 66 Interactive Visualization 67 3.5 Specialized Visualizations 70 Visualizing Networked Data 70 Visualizing Hierarchical Data: Treemaps 72 Visualizing Geographical Data: Map Charts 73 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 75 Prediction 75 Classification 75 Time Series Forecasting

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저자(글) Galit Shmueli

저자(글) Peter C. Bruce

저자(글) Nitin R. Patel