Description
Book SynopsisEmploy heuristic adjustments for truly accurate analysis Heuristics in Analytics presents an approach to analysis that accounts for the randomness of business and the competitive marketplace, creating a model that more accurately reflects the scenario at hand. With an emphasis on the importance of proper analytical tools, the book describes the analytical process from exploratory analysis through model developments, to deployments and possible outcomes. Beginning with an introduction to heuristic concepts, readers will find heuristics applied to statistics and probability, mathematics, stochastic, and artificial intelligence models, ending with the knowledge applications that solve business problems. Case studies illustrate the everyday application and implication of the techniques presented, while the heuristic approach is integrated into analytical modeling, graph analysis, text analytics, and more.
Robust analytics has become crucial in the corporate environ
Table of Contents
Preface xi
Acknowledgments xix
About the Authors xxiii
Chapter 1: Introduction 1
The Monty Hall Problem 5
Evolving Analytics 8
Summary 18
Chapter 2: Unplanned Events, Heuristics, and the Randomness in Our World 23
Heuristics Concepts 26
The Butterfly Effect 30
Random Walks 37
Summary 44
Chapter 3: The Heuristic Approach and Why We Use It 45
Heuristics in Computing 47
Heuristic Problem-Solving Methods 51
Genetic Algorithms: A Formal Heuristic Approach 54
Summary 67
Chapter 4: The Analytical Approach 69
Introduction to Analytical Modeling 71
The Competitive-Intelligence Cycle 74
Summary 97
Chapter 5: Knowledge Applications That Solve Business Problems 101
Customer Behavior Segmentation 102
Collection Models 106
Insolvency Prevention 113
Fraud-Propensity Models 120
Summary 127
Chapter 6: The Graph Analysis Approach 129
Introduction to Graph Analysis 130
Summary 143
Chapter 7: Graph Analysis Case Studies 147
Case Study: Identifying Influencers in Telecommunications 149
Case Study: Claim Validity Detection in Motor Insurance 162
Case Study: Fraud Identification in Mobile Operations 178
Summary 188
Chapter 8: Text Analytics 191
Text Analytics in the Competitive-Intelligence Cycle 193
Linguistic Models 198
Text-Mining Models 200
Summary 207
Bibliography 209
Index 217
