Handbook of statistical analysis and data mining applications /:
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook he...
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| Main Author: | |
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| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Amsterdam ; Boston :
Academic Press/Elsevier,
©2009.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 DE-862 DE-863 |
| Summary: | The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. |
| Physical Description: | 1 online resource (xxxiv, 824 pages) : illustrations (chiefly color) |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9780080912035 0080912036 9780123750860 0123750865 1282168312 9781282168312 9786612168314 6612168315 |
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| 245 | 1 | 0 | |a Handbook of statistical analysis and data mining applications / |c Robert Nisbet, John Elder, Gary Miner. |
| 246 | 1 | 4 | |a Handbook of statistical analysis & data mining applications |
| 260 | |a Amsterdam ; |a Boston : |b Academic Press/Elsevier, |c ©2009. | ||
| 300 | |a 1 online resource (xxxiv, 824 pages) : |b illustrations (chiefly color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 347 | |a text file | ||
| 520 | |a The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. | ||
| 505 | 0 | |a PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process -- Chapter 1. History -- The Phases of Data Analysis throughout the Ages -- Chapter 2. Theory -- Chapter 3. The Data Mining Process -- Chapter 4. Data Understanding and Preparation -- Chapter 5. Feature Selection -- Selecting the Best Variables -- Chapter 6: Accessory Tools and Advanced Features in Data -- PART II: -- The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools -- Chapter 7. Basic Algorithms -- Chapter 8: Advanced Algorithms -- Chapter 9. Text Mining -- Chapter 10. Organization of 3 Leading Data Mining Tools -- Chapter 11. Classification Trees = Decision Trees -- Chapter 12. Numerical Prediction (Neural Nets and GLM) -- Chapter 13. Model Evaluation and Enhancement -- Chapter 14. Medical Informatics -- Chapter 15. Bioinformatics -- Chapter 16. Customer Response Models -- Chapter 17. Fraud Detection -- PART III: Tutorials -- Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses -- Listing of Guest Authors of the Tutorials -- Tutorials within the book pages: -- How to use the DMRecipe -- Aviation Safety using DMRecipe -- Movie Box-Office Hit Prediction using SPSS CLEMENTINE -- Bank Financial data -- using SAS-EM -- Credit Scoring -- CRM Retention using CLEMENTINE -- Automobile -- Cars -- Text Mining -- Quality Control using Data Mining -- Three integrated tutorials from different domains, but all using C & RT to predict and display possible structural relationships among data: -- Business Administration in a Medical Industry -- Clinical Psychology- Finding Predictors of Correct Diagnosis -- Education -- Leadership Training: for Business and Education -- Additional tutorials are available either on the accompanying CD-DVD, or the Elsevier Web site for this book -- Listing of Tutorials on Accompanying CD -- PART IV: Paradox of Complex Models; using the "right model for the right use", on-going development, and the Future. -- Chapter 18: Paradox of Ensembles and Complexity -- Chapter 19: The Right Model for the Right Use -- Chapter 20: The Top 10 Data Mining Mistakes -- Chapter 21: Prospect for the Future -- Developing Areas in Data Mining. | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 546 | |a English. | ||
| 650 | 0 | |a Data mining |x Statistical methods. |0 http://id.loc.gov/authorities/subjects/sh2019001833 | |
| 650 | 6 | |a Exploration de données (Informatique) |x Méthodes statistiques. | |
| 650 | 7 | |a COMPUTERS |x Reference. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Machine Theory. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Computer Literacy. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Information Technology. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Computer Science. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Database Management |x Data Mining. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Hardware |x General. |2 bisacsh | |
| 650 | 7 | |a Data mining |x Statistical methods |2 fast | |
| 650 | 7 | |a Exploration de données |x Méthodes statistiques. |2 ram | |
| 700 | 1 | |a Elder, John F. |q (John Fletcher) |1 https://id.oclc.org/worldcat/entity/E39PCjB7WQPt66VrQqVWxYdb3P |0 http://id.loc.gov/authorities/names/no94013210 | |
| 700 | 1 | |a Miner, Gary. |1 https://id.oclc.org/worldcat/entity/E39PCjwG9dTYDmdRJRTVcwX4xC |0 http://id.loc.gov/authorities/names/n88045681 | |
| 758 | |i has work: |a Handbook of statistical analysis and data mining applications (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGtpKb8dDJ3bTRhxHdRD7b |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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| contents | PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process -- Chapter 1. History -- The Phases of Data Analysis throughout the Ages -- Chapter 2. Theory -- Chapter 3. The Data Mining Process -- Chapter 4. Data Understanding and Preparation -- Chapter 5. Feature Selection -- Selecting the Best Variables -- Chapter 6: Accessory Tools and Advanced Features in Data -- PART II: -- The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools -- Chapter 7. Basic Algorithms -- Chapter 8: Advanced Algorithms -- Chapter 9. Text Mining -- Chapter 10. Organization of 3 Leading Data Mining Tools -- Chapter 11. Classification Trees = Decision Trees -- Chapter 12. Numerical Prediction (Neural Nets and GLM) -- Chapter 13. Model Evaluation and Enhancement -- Chapter 14. Medical Informatics -- Chapter 15. Bioinformatics -- Chapter 16. Customer Response Models -- Chapter 17. Fraud Detection -- PART III: Tutorials -- Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses -- Listing of Guest Authors of the Tutorials -- Tutorials within the book pages: -- How to use the DMRecipe -- Aviation Safety using DMRecipe -- Movie Box-Office Hit Prediction using SPSS CLEMENTINE -- Bank Financial data -- using SAS-EM -- Credit Scoring -- CRM Retention using CLEMENTINE -- Automobile -- Cars -- Text Mining -- Quality Control using Data Mining -- Three integrated tutorials from different domains, but all using C & RT to predict and display possible structural relationships among data: -- Business Administration in a Medical Industry -- Clinical Psychology- Finding Predictors of Correct Diagnosis -- Education -- Leadership Training: for Business and Education -- Additional tutorials are available either on the accompanying CD-DVD, or the Elsevier Web site for this book -- Listing of Tutorials on Accompanying CD -- PART IV: Paradox of Complex Models; using the "right model for the right use", on-going development, and the Future. -- Chapter 18: Paradox of Ensembles and Complexity -- Chapter 19: The Right Model for the Right Use -- Chapter 20: The Top 10 Data Mining Mistakes -- Chapter 21: Prospect for the Future -- Developing Areas in Data Mining. |
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| discipline | Informatik |
| format | Electronic eBook |
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| indexdate | 2025-04-11T08:36:30Z |
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| spelling | Nisbet, Robert. https://id.oclc.org/worldcat/entity/E39PCjJyjb73PBQwDTfFq3Wcvb http://id.loc.gov/authorities/names/n2009015914 Handbook of statistical analysis and data mining applications / Robert Nisbet, John Elder, Gary Miner. Handbook of statistical analysis & data mining applications Amsterdam ; Boston : Academic Press/Elsevier, ©2009. 1 online resource (xxxiv, 824 pages) : illustrations (chiefly color) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process -- Chapter 1. History -- The Phases of Data Analysis throughout the Ages -- Chapter 2. Theory -- Chapter 3. The Data Mining Process -- Chapter 4. Data Understanding and Preparation -- Chapter 5. Feature Selection -- Selecting the Best Variables -- Chapter 6: Accessory Tools and Advanced Features in Data -- PART II: -- The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools -- Chapter 7. Basic Algorithms -- Chapter 8: Advanced Algorithms -- Chapter 9. Text Mining -- Chapter 10. Organization of 3 Leading Data Mining Tools -- Chapter 11. Classification Trees = Decision Trees -- Chapter 12. Numerical Prediction (Neural Nets and GLM) -- Chapter 13. Model Evaluation and Enhancement -- Chapter 14. Medical Informatics -- Chapter 15. Bioinformatics -- Chapter 16. Customer Response Models -- Chapter 17. Fraud Detection -- PART III: Tutorials -- Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses -- Listing of Guest Authors of the Tutorials -- Tutorials within the book pages: -- How to use the DMRecipe -- Aviation Safety using DMRecipe -- Movie Box-Office Hit Prediction using SPSS CLEMENTINE -- Bank Financial data -- using SAS-EM -- Credit Scoring -- CRM Retention using CLEMENTINE -- Automobile -- Cars -- Text Mining -- Quality Control using Data Mining -- Three integrated tutorials from different domains, but all using C & RT to predict and display possible structural relationships among data: -- Business Administration in a Medical Industry -- Clinical Psychology- Finding Predictors of Correct Diagnosis -- Education -- Leadership Training: for Business and Education -- Additional tutorials are available either on the accompanying CD-DVD, or the Elsevier Web site for this book -- Listing of Tutorials on Accompanying CD -- PART IV: Paradox of Complex Models; using the "right model for the right use", on-going development, and the Future. -- Chapter 18: Paradox of Ensembles and Complexity -- Chapter 19: The Right Model for the Right Use -- Chapter 20: The Top 10 Data Mining Mistakes -- Chapter 21: Prospect for the Future -- Developing Areas in Data Mining. Includes bibliographical references and index. Print version record. English. Data mining Statistical methods. http://id.loc.gov/authorities/subjects/sh2019001833 Exploration de données (Informatique) Méthodes statistiques. COMPUTERS Reference. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Computer Literacy. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Database Management Data Mining. bisacsh COMPUTERS Hardware General. bisacsh Data mining Statistical methods fast Exploration de données Méthodes statistiques. ram Elder, John F. (John Fletcher) https://id.oclc.org/worldcat/entity/E39PCjB7WQPt66VrQqVWxYdb3P http://id.loc.gov/authorities/names/no94013210 Miner, Gary. https://id.oclc.org/worldcat/entity/E39PCjwG9dTYDmdRJRTVcwX4xC http://id.loc.gov/authorities/names/n88045681 has work: Handbook of statistical analysis and data mining applications (Text) https://id.oclc.org/worldcat/entity/E39PCGtpKb8dDJ3bTRhxHdRD7b https://id.oclc.org/worldcat/ontology/hasWork Print version: Nisbet, Robert. Handbook of statistical analysis and data mining applications. Amsterdam ; Boston : Academic Press/Elsevier, ©2009 9780123747655 (DLC) 2009008997 (OCoLC)316327105 |
| spellingShingle | Nisbet, Robert Handbook of statistical analysis and data mining applications / PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process -- Chapter 1. History -- The Phases of Data Analysis throughout the Ages -- Chapter 2. Theory -- Chapter 3. The Data Mining Process -- Chapter 4. Data Understanding and Preparation -- Chapter 5. Feature Selection -- Selecting the Best Variables -- Chapter 6: Accessory Tools and Advanced Features in Data -- PART II: -- The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining Tools -- Chapter 7. Basic Algorithms -- Chapter 8: Advanced Algorithms -- Chapter 9. Text Mining -- Chapter 10. Organization of 3 Leading Data Mining Tools -- Chapter 11. Classification Trees = Decision Trees -- Chapter 12. Numerical Prediction (Neural Nets and GLM) -- Chapter 13. Model Evaluation and Enhancement -- Chapter 14. Medical Informatics -- Chapter 15. Bioinformatics -- Chapter 16. Customer Response Models -- Chapter 17. Fraud Detection -- PART III: Tutorials -- Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining Analyses -- Listing of Guest Authors of the Tutorials -- Tutorials within the book pages: -- How to use the DMRecipe -- Aviation Safety using DMRecipe -- Movie Box-Office Hit Prediction using SPSS CLEMENTINE -- Bank Financial data -- using SAS-EM -- Credit Scoring -- CRM Retention using CLEMENTINE -- Automobile -- Cars -- Text Mining -- Quality Control using Data Mining -- Three integrated tutorials from different domains, but all using C & RT to predict and display possible structural relationships among data: -- Business Administration in a Medical Industry -- Clinical Psychology- Finding Predictors of Correct Diagnosis -- Education -- Leadership Training: for Business and Education -- Additional tutorials are available either on the accompanying CD-DVD, or the Elsevier Web site for this book -- Listing of Tutorials on Accompanying CD -- PART IV: Paradox of Complex Models; using the "right model for the right use", on-going development, and the Future. -- Chapter 18: Paradox of Ensembles and Complexity -- Chapter 19: The Right Model for the Right Use -- Chapter 20: The Top 10 Data Mining Mistakes -- Chapter 21: Prospect for the Future -- Developing Areas in Data Mining. Data mining Statistical methods. http://id.loc.gov/authorities/subjects/sh2019001833 Exploration de données (Informatique) Méthodes statistiques. COMPUTERS Reference. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Computer Literacy. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Database Management Data Mining. bisacsh COMPUTERS Hardware General. bisacsh Data mining Statistical methods fast Exploration de données Méthodes statistiques. ram |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2019001833 |
| title | Handbook of statistical analysis and data mining applications / |
| title_alt | Handbook of statistical analysis & data mining applications |
| title_auth | Handbook of statistical analysis and data mining applications / |
| title_exact_search | Handbook of statistical analysis and data mining applications / |
| title_full | Handbook of statistical analysis and data mining applications / Robert Nisbet, John Elder, Gary Miner. |
| title_fullStr | Handbook of statistical analysis and data mining applications / Robert Nisbet, John Elder, Gary Miner. |
| title_full_unstemmed | Handbook of statistical analysis and data mining applications / Robert Nisbet, John Elder, Gary Miner. |
| title_short | Handbook of statistical analysis and data mining applications / |
| title_sort | handbook of statistical analysis and data mining applications |
| topic | Data mining Statistical methods. http://id.loc.gov/authorities/subjects/sh2019001833 Exploration de données (Informatique) Méthodes statistiques. COMPUTERS Reference. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Computer Literacy. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Database Management Data Mining. bisacsh COMPUTERS Hardware General. bisacsh Data mining Statistical methods fast Exploration de données Méthodes statistiques. ram |
| topic_facet | Data mining Statistical methods. Exploration de données (Informatique) Méthodes statistiques. COMPUTERS Reference. COMPUTERS Machine Theory. COMPUTERS Computer Literacy. COMPUTERS Information Technology. COMPUTERS Data Processing. COMPUTERS Computer Science. COMPUTERS Database Management Data Mining. COMPUTERS Hardware General. Data mining Statistical methods Exploration de données Méthodes statistiques. |
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