Heuristics in analytics : a practical perspective of what influences our analytical world /

• Κύριοι συγγραφείς: Reis Pinheiro, Carlos Andre, 1940- (Συγγραφέας), McNeill, Fiona (Συγγραφέας)
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Hoboken, New Jersey : Wiley, [2014]
• Σειρά: Wiley and SAS business series.
• Θέματα: Management > Statistical methods.; Decision making > Statistical methods.; Business planning > Statistical methods.; Heuristic algorithms.; System analysis.; Electronic books.

Employ 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...

Πίνακας περιεχομένων:

• Unplanned events, heuristics, and the randomness of the world
• The heuristic approach and why we use it
• The analytical approach
• Knowledge applications that solve business problems
• The graph analysis approach
• Graph analysis case studies
• Text analytics.
• Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World; Copyright; Contents; Preface; Acknowledgments; About the Authors; Chapter 1: Introduction; The Monty Hall Problem; Evolving Analytics; The Business Relevance of Analytics; The Role of Analytics in Innovation; Innovation in a Changing World; Summary; Chapter 2: Unplanned Events, Heuristics, and the Randomness in Our World; Heuristics Concepts; Heuristics in Operations; The Butterfly Effect; Random Walks; The Drunkard's Walk; Probability and Chance; Summary.
• Chapter 3: The Heuristic Approach and Why We Use It Heuristics in Computing; Heuristic Problem-Solving Methods; Genetic Algorithms: A Formal Heuristic Approach; Foundation of Genetic Algorithms; Initialization; Selection; Reproduction; Termination; Pseudo-Code Algorithm; Benefits of Genetic Algorithms; Influences in Competitive Industries; Genetic Algorithms Solving Business Problems; Summary; Chapter 4: The Analytical Approach; Introduction to Analytical Modeling; The Competitive-Intelligence Cycle; Data; Information; Knowledge; Intelligence; Experience; Summary.
• Chapter 5: Knowledge Applications That Solve Business Problems customer Behavior Segmentation; Collection Models; Insolvency Segmentation; Collection Notice Recovery; Anticipating Revenue from Collection Actions; Insolvency Prevention; Bad-Debt Classification; Avoiding Taxes; Fraud-Propensity Models; New Fraud Detection; Classifying Fraudulent Usage Behavior; Summary; Chapter 6: The Graph Analysis Approach; Introduction to Graph Analysis; Graphs Structures, Network Metrics, and Analyses Approaches; Network Metrics; Types of Subgraphs; Summary; Chapter 7: Graph Analysis Case Studies.
• Case Study: Identifying Influencers in Telecommunications background in Churn and Sales; Internal Networks; Customer Influence; Customer Influence and Business Event Correlation; Possible Business Applications and Final Figures in Churn and Sales; Case Study: Claim Validity Detection in Motor Insurance; Background in Insurance and Claims; Network Definition; Participant Networks; Group Analysis; Identifying Outliers; Final Figures in Claims; Visualizing for More Insight; Final Figures in Insurance Exaggeration; Case Study: Fraud Identification in Mobile Operations.
• Background in Telecommunications Fraud Social Networks and Fraud; Community Detection; Finding the Outliers within Communities; Rules and Thresholds for Community Outliers; Fraudsters Visualization; Final Figures in Fraud; Summary; Chapter 8: Text Analytics; Text Analytics in the Competitive-Intelligence Cycle; Information Revisited; Knowledge Revisited; Linguistic Models; Text-Mining Models; Intelligence Revisited; Experience Revisited; Summary; Bibliography; Index.