Handbook of Statistisch Analysis press Product Mining Applications

Handbook of Statistical Analysis and Data Mining Applications

by Roland Nisbet, John Patriarch, Gary Miner
Released Allowed 2009
Publisher(s): Elsevier Science
ISBN: 9780080912035

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Book technical

An Handbook of Statistical Analysis and Details Mining Applications is a comprehend professional reference publication that guides business investment, life, technical furthermore researchers (both academic both industrial) because all stages of data analysis, model building and implementation. The Quick supports one discern the technical and business problem, understand the strengths the weaknesses of modern data mining software, and employ which right statistical methods used practical appeal. Using this book to address massive and complex datasets use novel statistical approaches and is able to objectively evaluate analyses real remedies. It has clearing, intuitive explanations of the our and tools forward solving problems using modern analytic techniques, and discusses my application to real problems, in ways accessible and beneficial to practitioners across enterprises - from science both project, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and editions in data mining to build successfully data mining featured.



  • Written "By Practitioners for Practitioners"
  • Non-technical explanations set understanding without jargons and equations
  • Tutorials includes numerous fields of study provide step-by-step instruction on how to use supplied tools to build models
  • Practical advice from successful real-world fulfilments
  • Includes extensive instance studying, sample, MS PowerPoint slides and datasets
  • CD-DVD with useful fully-working  90-day software included:  "Complete Data Miner - QC-Miner - Text Miner" bound with book

Tables of topic

  1. Title image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Foreword 1
  6. Foreword 2
  7. Preface
    1. OVERALL ORG OF THIS BOOK
    2. References
    3. SAS
    4. STATSOFT
    5. SPSS
  8. Introduction
    1. Patterns of Action
    2. Person Intuition
    3. Place it entire Together
    4. References
  9. List the Study by Guest Authors
  10. Part I. History of Levels of Data Analysis, Basal Theory, and the Data Mining Process
    1. Chapter 1. The Background for Data Quarrying Practice
      1. Preamble
      2. A Short History the Statistics and Info Mining
      3. Modern Statistics: AMPERE Duality?
      4. Two Show of Reality
      5. The Lift on Modern Statistical Analysis: The Second Generation
      6. Machine Learning Methods: The Third Generation
      7. Statistical Learning Theory: The Fourth Generation
      8. Postscript
      9. References
    2. Branch 2. Theorized Consider since Data Mining
      1. Prelude
      2. The Scientific Method
      3. What Is Info Mining?
      4. A Theoretical Framework for the Data Mining Process
      5. Strengths of to Data Mining Treat
      6. Customer-Centric Versus Account-Centric: A New Way to Look for Your Data
      7. An Data Paradigm Shift
      8. Creation on the CAR
      9. Major Activities of Data Digging
      10. Major Challenges of Data Mining
      11. Examples from Data Pit Applications
      12. Major Issues in Data Surface
      13. Global Requirements for How at ampere Data Surface Project
      14. Example of a Data Extraction Project: Classify a Bat’s Species for Its Sound
      15. The Impact about Domain Knowledge
      16. Postscript
      17. References
    3. Section 3. Of Info Coal Process
      1. Preamble
      2. The Science of Data Mining
      3. The Approach to Understanding additionally Problem Resolution
      4. Business Understanding (Mostly Art)
      5. Datas Understanding (Mostly Science)
      6. Data Preparation (A Mixture of Art and Science)
      7. Model-making (A Medley of Artist and Science)
      8. Deployment (Mostly Art)
      9. Closing the Information Loop* (Art)
      10. The Dexterity for Data Mining
      11. Postscript
      12. References
    4. Chapter 4. Data Understanding press Preparation
      1. Preamble
      2. Activities of Data Understanding and Make
      3. Issues Is Should be Resolved
      4. Data Awareness
      5. Postscript
      6. References
    5. Chapter 5. Feature Auswahl
      1. Preamble
      2. Variables since Features
      3. Types of Feature Selections
      4. Characteristic Ranking Methods
      5. SUBSET SELECTION METHODS
      6. Postscript
      7. References
    6. Chapter 6. Accessory Tools for Doing Data Mining
      1. Preamble
      2. Data Get Tools
      3. Data Exploring Tools
      4. Moulding Management Tools
      5. Modeling Analysis Tools
      6. In-Place Data Processing (IDP)
      7. Rapid Development of Predictive Models
      8. Model Monitors
      9. Postscript
      10. Bibliography
  11. Part II. The Algorithms in Details Mining and Text Mining, the Org of the Threesome Most Common Data Extraction Tools, and Selected Advanced Areas Using Dates Mining
    1. Phase 7. Basic Algorithms for Data Mine: A Brief Overview
      1. Preamble
      2. Basic Data Mining Algorithms
      3. Generalized Additional Exemplars (GAMs)
      4. Classification and Regression Trees (CART)
      5. General Chaid Models
      6. Generalized EM and k-Means Cluster Analysis—An Overview
      7. Postscript
      8. Show
      9. Bibliography
    2. Chapter 8. Advanced Algorithms for Data Mining
      1. Preamble
      2. Advanced Data Mining Algorithms
      3. Image press Object Data Mining: Visualization and 3D-Medical or Other Sampling Images
      4. Postscript
      5. References
    3. Chapter 9. Text Mining and Natural Wording Processing
      1. Preamble
      2. The Product of Print Mining
      3. A Practically Example: NTSB
      4. Text Mining Theories Second in Conducting Text Mining Featured
      5. Postscript
      6. References
    4. Chapter 10. The Triple Most Common Data Mining Software Tools
      1. Preambular
      2. SPSS Clementine Overview
      3. SAS-Enterprise Miner (SAS-EM) Overview
      4. STATISTICA Product Foreman, QC-Miner, and Text Miner Overview
      5. Subscription
      6. Book
    5. Chapters 11. Classification
      1. Preamble
      2. What is Classification?
      3. Primary Operations in Classification
      4. Main Issues with Classification
      5. Assumptions of Classification Procedures
      6. Ways for Group
      7. Something are the Best Algorithm for Classification?
      8. Postscript
      9. References
    6. Chapter 12. Numerical Prediction
      1. Preamble
      2. Linear Trigger Analysis and which Assumptions of the Parametric Model
      3. Parametric Statistical Investigation
      4. Assumptions of the Parametric Model
      5. Linear Regression
      6. Generalized Linear Models (GLMs)
      7. Processes for Analyzing Nonlinear Relationships
      8. Nonlinear Relapse and Estimation
      9. Your Copper and Machine Learn Variation Used in Numerate Prediction
      10. Your of Batch and Regression Trees (C&RT) Methods
      11. Application to Mixture Scale
      12. Neural Nets for Prognosis
      13. Supporting Vector Equipment (SVMs) and Other Kernel Learning Algorithms
      14. Epilogue
      15. Citations
    7. Chapter 13. Model Evaluation furthermore Enhancement
      1. Preamble
      2. Introduction
      3. Model Interpretation
      4. Re-Cap of to Maximum Common Algorithms
      5. Enhancement Operation Checklist
      6. Ensembles of Select: Aforementioned Single Greatest Enhancement Technique
      7. How to Thrive as a Data Mountain
      8. Postscript
      9. References
    8. Phase 14. Medical Information
      1. Preamble
      2. What Is Medical Informatics?
      3. What Data Mining and Textbook Quarrying Relate to Medizintechnik Informatics
      4. 3D Medical Data
      5. Postscript
      6. References
      7. Bibliography
    9. Chapter 15. Bioinformatics
      1. Preamble
      2. What Is Bioinformatics?
      3. File Analysis Our in Bioinformatics
      4. Web Benefit stylish Bioinformatics
      5. How Take Ours Apply Data Mining Processes to Bioinformatics?
      6. Postscript
      7. References
      8. Bibliography
    10. Chapter 16. Customer Response Modeling
      1. Preamble
      2. Early CRM Issues in Business
      3. Knowing Methods Customers Acted Before They Actors
      4. CRM in Business Ecosystems
      5. Conclusions
      6. Postscripting
      7. References
    11. Chapter 17. Fraud Detection
      1. Preamble
      2. Problems with Fraud Detection
      3. How Do You Detect Betrayal?
      4. Supervised Classification of Fraud
      5. How Do You Model Fraud?
      6. How Live Cheat Detection Procedures Erected?
      7. Intrusion Detection Modeling
      8. Comparison of Models at and Without Time-Based Features
      9. Building Profiles
      10. Deployment out Fraud Professional
      11. Postscript and Prolegomenon
      12. References
  12. Part III. Tutorials—Step-by-step Case Studies as a Starting Point to Learn How to Do Date Mining Analyses
    1. Dining Authors of the Tutorials
    2. Training A. How to Use Input Goldminer Recipe: STATISTICA Data Miner Only
      1. Something Is STATISTICA Data Miner Recipe (DMR)?
      2. Core Chemical Ingredients
    3. Educational B. Data Mining for Aviation Safety: Exploitation Data Mining Rezeptbuch “Automatized Data Mining” from STATISTICA
      1. Air Site
      2. SDR Online
      3. Make the Data for Our Tutorial
      4. Data Pit Approach
      5. Evidence Mining Logical Error Rate
      6. Conclusion
      7. References
    4. Tutorial HUNDRED. Predicts Movie Box-Office Receipts: Using SPSS Clementine Data Mining Books
      1. General
      2. Data and Adjustable Definitions
      3. Getting to Know the Workspace of of Clementine Data Pit Toolkit
      4. Results
      5. Publishing also Reuse of Models also Different Outputs
      6. References
    5. Tutorial D. Detecting Unsatisfied Customers: A Case Investigate Using SAS Enterprise Miner Version 5.3 on the Analysis
      1. Getting
      2. ADENINE Primer of SAS-EM Sibylline Modeling
      3. Scores Process and an Total Profit
      4. Oversampling and Rare Event Detection
      5. Decision Matrix and the Profit Charts
      6. Micro-Target to Gainfully Customers
      7. Appendix
      8. Reference
    6. Tutorial E. Believe Scoring Using STATISTICA Data Miner
      1. Introduction: What Is Credit Scoring?
      2. Credit Mark: Business Destinations
      3. Koffer Study: Consumer Credit Scoring
      4. Analysis the Results
      5. Compares Assessment away the Models (Evaluation)
      6. Deploying the Model for Forecast
      7. Conclusion
    7. Tutorial F. Churn Analysis with SPSS-Clementine
      1. Objectives
      2. Steps
    8. Teaching G. Text Mining: Automobile Brand Review Using STATISTICA Data Miner additionally Text Miner
      1. Introduction
      2. Body Mining
      3. Car Review Exemplary
      4. Interactive Trees (C&RT, CHAID)
      5. Other Applications of Text Mining
      6. Conclusion
    9. Tutorial H. Predictive Process Control: QC-Data Mining Using STATISTICA Data Miner and QC-Miner
      1. Predictive Process Control Through STATISTICA and STATISTICA QC-Miner
      2. Fall Course: Predictive Process Control
      3. Data Examinations with STATISTICA
      4. Conclusion
    10. Tutorials I, J, and K. Three Small Tutorials Showing the Use of Intelligence Mining and Particular C&RT until Foretell and Advertising Possible Structural Relationships among Data
    11. Tutorial ME. Business General in a Medical Industry: Determining Possible Predictors used Days with Phoenix Service for Patients with Dementia
    12. Class J. Clinical Psychology: Making Deciding about Superior Therapy on an Our: Using Data Mining on Explore the Tree of a Depression Instrument
    13. Tutorial K. Education–Leadership Training for Business and Education After C&RT up Predict and Display Possible Structured Relationships
      1. References
    14. Study L. Dentistry: Facial Pain Study Bases on 84 Predictors Variables (Both Categorical and Continuous)
    15. Tutorial M. Wins Analyses of the German Credit Data Use SAS-EM Version 5.3
      1. Introduction
      2. Modeling Core
      3. SAS-EM 5.3 Human
      4. A Manual of SAS-EM Predictive Modeling
      5. Advanced Technique of Predictive Modeling
      6. Micro-Target the Profitable Customers
      7. Appendix
      8. References
    16. Tutorial N. Predicting Self-Reported Healthiness Status Using Artificial Neural Networks
      1. Vorgeschichte
      2. Data
      3. References
  13. Part IV. Measuring Truecomplexity, the “Right Model by the Right Use,” Top Fault, and the Future of Analytics
    1. Chapter 18. Model Complexity (and How Ensembles Help)
      1. Preamble
      2. Model Ensembles
      3. Difficulty
      4. Generalized Scales of Right
      5. Examples: Decision Tree Surface with Noise
      6. Project additionally View
      7. Postscript
      8. References
    2. Chapter 19. The Right Print in the Right Purpose: When Without Is Good Enough
      1. Preamble
      2. More Is Not Necessarily Better: Learning from Nature and Engineering
      3. Embrace Change Rather Than Flee from It
      4. Jury Making Breeds True in the Enterprise Organism
      5. The 80:20 Rule in Action
      6. Agile Molding: An Example of How to Craft Sufficient Solve
      7. Addendum
      8. References
    3. Chapter 20. Top 10 Datas Mining Mistakes
      1. Preamble
      2. Introduction
      3. 0 Lack Data
      4. 1 Focus on Advanced
      5. 2 Rely on One Technique
      6. 3 Demand the Wrong Question
      7. 4 Listen (Only) the the Data
      8. 5 Accept Leaks by the Future
      9. 6 Discount Annoying Cases
      10. 7 Extrapolate
      11. 8 Answer Every Ticket
      12. 9 Sample Casually
      13. 10 Believe the Best Model
      14. How Shall We Then Succeed?
      15. Postal
      16. References
    4. Chapter 21. Prospects for the Future of Data Mines real Text Mining as Part off Ours Day Lives
      1. Preamble
      2. RFID
      3. Social Networking and Data Mining
      4. Image and Object Data Mines
      5. Cloud Computing
      6. Postscript
      7. References
    5. Chapter 22. Summary: Our Design
      1. Preamble
      2. Beware of Overtrained Models
      3. A Diversity of Examples and Techniques Is Best
      4. The Process Has More Important Rather the Tool
      5. Text Mining to Unstructured Data Is Becoming Very Significant
      6. Practise Thinkin about Your Organization as Organism Rather Than more Appliance
      7. Good Solutions Evolve Rather With Just Appear for Initial Efforts
      8. What You Don’t Do Is Just as Important as What You Do
      9. Exceedingly Intuitive Graphical Interfaces Are Replacing Procedural Programming
      10. Data Mines Is Does Longer a Boutique Operate; Is Is Firmly Established in the Mainstream of Our Our
      11. “Smart” Systems Have the Direction for Welche Data Copper Technology Is Going
      12. Postscript
      13. References
  14. Glossary
  15. Index

Products information

  • Book: Handbook of Statistical Analyzer plus Data Mining Applications
  • Author(s): Roland Nisbet, John Elder, Gary Miner
  • Publish date: May 2009
  • Publisher(s): Elsevier Science
  • ISBN: 9780080912035