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Handbook of statistical analysis and data mining applications [[electronic resource] /] / Robert Nisbet, John Elder, Gary Miner
| Handbook of statistical analysis and data mining applications [[electronic resource] /] / Robert Nisbet, John Elder, Gary Miner |
| Autore | Nisbet Robert |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Academic Press/Elsevier, c2009 |
| Descrizione fisica | 1 online resource (859 p.) |
| Disciplina |
006.3/12 22
519.5 |
| Altri autori (Persone) |
ElderJohn F (John Fletcher)
MinerGary |
| Soggetto topico |
Data mining - Statistical methods
Multivariate analysis |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-16831-2
9786612168314 0-08-091203-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Handbook of Statistical Analysis and Data Mining Applications; Copyright Page; Table of Contents; Foreword 1; Foreword 2; Preface; Introduction; List of Tutorials by Guest Authors; Part 1: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process; Chapter 1: The Background for Data Mining Practice; Assumptions of the Parametric Model; Two Views of Reality; Aristotle; Plato; The Rise of Modern Statistical Analysis: The Second Generation; Machine Learning Methods: The Third Generation; Statistical Learning Theory: The Fourth Generation
Chapter 2: Theoretical Considerations for Data MiningMajor Issues in Data Mining; General Requirements for Success in a Data Mining Project; The Importance of Domain Knowledge; Postscript; Some Caveats with Data Mining Solutions; Chapter 3: The Data Mining Process; CRISP-DM; Assess the Business Environment for Data Mining; Data Understanding (Mostly Science); References; Preamble; Chapter 4: Data Understanding and Preparation; Preamble; Issues That Should be Resolved; Splitting Data Part 1: Using a Wrapper Approach in Weka to Determine the Most Appropriate Variables for Your Neural Network ModelExample 4; Data Extraction; Data Weighting and Balancing; Data Filtering and Smoothing; Data Abstraction; Data Reduction; Data Sampling; Data Discretization; Data Derivation; Postscript; Chapter 5: Feature Selection; Inductive Database Approach; Bi-variate Methods; Multivariate Methods; Postscript; Complex Methods; The Other Two Ways of Using Feature Selection in STATISTICA: Interactive Workspace; Preamble; Chapter 6: Accessory Tools for Doing Data Mining; Preamble; Introduction Basic Descriptive StatisticsCombining Groups (Classes) for Predictive Data Mining; Generalized Linear Models (GLMs); Data Miner Workspace Templates; Comparison of Models with and Without Time-Based Features; Example: The IDP Facility of STATISTICA Data Miner; Ensembles in General; Part 2: The Algorithms in Data Mining and Text Mining, the Organization of the Three most common Data Mining Tools, and Selected Speci...; Chapter 7: Basic Algorithms for Data Mining: A Brief Overview; Preamble; STATISTICA Data Miner Recipe (DMRecipe); Automated Neural Nets; Generalized Additive Models (GAMs) Outputs of GAMsRecursive Partitioning; Pruning Trees; Bibliography; Chapter 8: Advanced Algorithms for Data Mining; The Physical Data Mart; Summary; Micro-Target the Profitable Customers; Quality Control Data Mining and Root Cause Analysis; Chapter 9: Text Mining and Natural Language Processing; The Development of Text Mining; Chapter 10: The Three Most Common Data Mining Software Tools; Preamble; SPSS Clementine Overview; Preamble; Setting the Default Directory; Visual Data Preparation for Data Mining: Taking Photos, Moving Pictures, and Objects into Spreadsheets Representing the Photos... Preamble |
| Record Nr. | UNINA-9910455538003321 |
Nisbet Robert
|
||
| Amsterdam ; ; Boston, : Academic Press/Elsevier, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of statistical analysis and data mining applications [[electronic resource] /] / Robert Nisbet, John Elder, Gary Miner
| Handbook of statistical analysis and data mining applications [[electronic resource] /] / Robert Nisbet, John Elder, Gary Miner |
| Autore | Nisbet Robert |
| Pubbl/distr/stampa | Amsterdam ; ; Boston, : Academic Press/Elsevier, c2009 |
| Descrizione fisica | 1 online resource (859 p.) |
| Disciplina |
006.3/12 22
519.5 |
| Altri autori (Persone) |
ElderJohn F (John Fletcher)
MinerGary |
| Soggetto topico |
Data mining - Statistical methods
Multivariate analysis |
| ISBN |
1-282-16831-2
9786612168314 0-08-091203-6 |
| Classificazione | 31.73 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Handbook of Statistical Analysis and Data Mining Applications; Copyright Page; Table of Contents; Foreword 1; Foreword 2; Preface; Introduction; List of Tutorials by Guest Authors; Part 1: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process; Chapter 1: The Background for Data Mining Practice; Assumptions of the Parametric Model; Two Views of Reality; Aristotle; Plato; The Rise of Modern Statistical Analysis: The Second Generation; Machine Learning Methods: The Third Generation; Statistical Learning Theory: The Fourth Generation
Chapter 2: Theoretical Considerations for Data MiningMajor Issues in Data Mining; General Requirements for Success in a Data Mining Project; The Importance of Domain Knowledge; Postscript; Some Caveats with Data Mining Solutions; Chapter 3: The Data Mining Process; CRISP-DM; Assess the Business Environment for Data Mining; Data Understanding (Mostly Science); References; Preamble; Chapter 4: Data Understanding and Preparation; Preamble; Issues That Should be Resolved; Splitting Data Part 1: Using a Wrapper Approach in Weka to Determine the Most Appropriate Variables for Your Neural Network ModelExample 4; Data Extraction; Data Weighting and Balancing; Data Filtering and Smoothing; Data Abstraction; Data Reduction; Data Sampling; Data Discretization; Data Derivation; Postscript; Chapter 5: Feature Selection; Inductive Database Approach; Bi-variate Methods; Multivariate Methods; Postscript; Complex Methods; The Other Two Ways of Using Feature Selection in STATISTICA: Interactive Workspace; Preamble; Chapter 6: Accessory Tools for Doing Data Mining; Preamble; Introduction Basic Descriptive StatisticsCombining Groups (Classes) for Predictive Data Mining; Generalized Linear Models (GLMs); Data Miner Workspace Templates; Comparison of Models with and Without Time-Based Features; Example: The IDP Facility of STATISTICA Data Miner; Ensembles in General; Part 2: The Algorithms in Data Mining and Text Mining, the Organization of the Three most common Data Mining Tools, and Selected Speci...; Chapter 7: Basic Algorithms for Data Mining: A Brief Overview; Preamble; STATISTICA Data Miner Recipe (DMRecipe); Automated Neural Nets; Generalized Additive Models (GAMs) Outputs of GAMsRecursive Partitioning; Pruning Trees; Bibliography; Chapter 8: Advanced Algorithms for Data Mining; The Physical Data Mart; Summary; Micro-Target the Profitable Customers; Quality Control Data Mining and Root Cause Analysis; Chapter 9: Text Mining and Natural Language Processing; The Development of Text Mining; Chapter 10: The Three Most Common Data Mining Software Tools; Preamble; SPSS Clementine Overview; Preamble; Setting the Default Directory; Visual Data Preparation for Data Mining: Taking Photos, Moving Pictures, and Objects into Spreadsheets Representing the Photos... Preamble |
| Record Nr. | UNINA-9910777944903321 |
|
Nisbet Robert
|
||
| Amsterdam ; ; Boston, : Academic Press/Elsevier, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of statistical analysis and data mining applications / / Robert Nisbet, John Elder, Gary Miner
| Handbook of statistical analysis and data mining applications / / Robert Nisbet, John Elder, Gary Miner |
| Autore | Nisbet Robert |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Amsterdam ; ; Boston : , : Academic Press/Elsevier, , c2009 |
| Descrizione fisica | 1 online resource (859 pages) |
| Disciplina |
006.3/12 22
519.5 |
| Altri autori (Persone) |
ElderJohn F (John Fletcher)
MinerGary |
| Soggetto topico |
Data mining - Statistical methods
Multivariate analysis |
| ISBN |
1-282-16831-2
9786612168314 0-08-091203-6 |
| Classificazione | 31.73 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Handbook of Statistical Analysis and Data Mining Applications; Copyright Page; Table of Contents; Foreword 1; Foreword 2; Preface; Introduction; List of Tutorials by Guest Authors; Part 1: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process; Chapter 1: The Background for Data Mining Practice; Assumptions of the Parametric Model; Two Views of Reality; Aristotle; Plato; The Rise of Modern Statistical Analysis: The Second Generation; Machine Learning Methods: The Third Generation; Statistical Learning Theory: The Fourth Generation
Chapter 2: Theoretical Considerations for Data MiningMajor Issues in Data Mining; General Requirements for Success in a Data Mining Project; The Importance of Domain Knowledge; Postscript; Some Caveats with Data Mining Solutions; Chapter 3: The Data Mining Process; CRISP-DM; Assess the Business Environment for Data Mining; Data Understanding (Mostly Science); References; Preamble; Chapter 4: Data Understanding and Preparation; Preamble; Issues That Should be Resolved; Splitting Data Part 1: Using a Wrapper Approach in Weka to Determine the Most Appropriate Variables for Your Neural Network ModelExample 4; Data Extraction; Data Weighting and Balancing; Data Filtering and Smoothing; Data Abstraction; Data Reduction; Data Sampling; Data Discretization; Data Derivation; Postscript; Chapter 5: Feature Selection; Inductive Database Approach; Bi-variate Methods; Multivariate Methods; Postscript; Complex Methods; The Other Two Ways of Using Feature Selection in STATISTICA: Interactive Workspace; Preamble; Chapter 6: Accessory Tools for Doing Data Mining; Preamble; Introduction Basic Descriptive StatisticsCombining Groups (Classes) for Predictive Data Mining; Generalized Linear Models (GLMs); Data Miner Workspace Templates; Comparison of Models with and Without Time-Based Features; Example: The IDP Facility of STATISTICA Data Miner; Ensembles in General; Part 2: The Algorithms in Data Mining and Text Mining, the Organization of the Three most common Data Mining Tools, and Selected Speci...; Chapter 7: Basic Algorithms for Data Mining: A Brief Overview; Preamble; STATISTICA Data Miner Recipe (DMRecipe); Automated Neural Nets; Generalized Additive Models (GAMs) Outputs of GAMsRecursive Partitioning; Pruning Trees; Bibliography; Chapter 8: Advanced Algorithms for Data Mining; The Physical Data Mart; Summary; Micro-Target the Profitable Customers; Quality Control Data Mining and Root Cause Analysis; Chapter 9: Text Mining and Natural Language Processing; The Development of Text Mining; Chapter 10: The Three Most Common Data Mining Software Tools; Preamble; SPSS Clementine Overview; Preamble; Setting the Default Directory; Visual Data Preparation for Data Mining: Taking Photos, Moving Pictures, and Objects into Spreadsheets Representing the Photos... Preamble |
| Record Nr. | UNINA-9910823172403321 |
|
Nisbet Robert
|
||
| Amsterdam ; ; Boston : , : Academic Press/Elsevier, , c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
KDD '21 : Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining / / Feida Zhu [and six others]
| KDD '21 : Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining / / Feida Zhu [and six others] |
| Autore | Zhu Feida |
| Pubbl/distr/stampa | New York, NY : , : Association for Computing Machinery, , 2021 |
| Descrizione fisica | 1 online resource (57 pages) |
| Disciplina | 006.312 |
| Soggetto topico |
Data mining
Data mining - Statistical methods |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910510461803321 |
|
Zhu Feida
|
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| New York, NY : , : Association for Computing Machinery, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Knowledge discovery in cyberspace : statistical analysis and predictive modeling / / Kristijan Kuk and Dragan Ranđelović, editors
| Knowledge discovery in cyberspace : statistical analysis and predictive modeling / / Kristijan Kuk and Dragan Ranđelović, editors |
| Pubbl/distr/stampa | New York : , : Nova Publishers, , [2017] |
| Descrizione fisica | 1 online resource (218 pages) : illustrations (some color), color maps |
| Disciplina | 363.25/968 |
| Collana | Cybercrime and cybersecurity research |
| Soggetto topico |
Data mining - Statistical methods
Computer crimes - Investigation Computer crimes - Prevention Cyberspace |
| ISBN | 1-5361-0570-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910156163403321 |
| New York : , : Nova Publishers, , [2017] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Seriation in combinatorial and statistical data analysis / / Israël César Lerman and Henri Leredde
| Seriation in combinatorial and statistical data analysis / / Israël César Lerman and Henri Leredde |
| Autore | Lerman Israël César |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (287 pages) |
| Disciplina | 006.312 |
| Collana | Advanced Information and Knowledge Processing |
| Soggetto topico |
Data mining - Statistical methods
Combinatorial analysis - Data processing |
| ISBN | 3-030-92694-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910551833003321 |
|
Lerman Israël César
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Seriation in combinatorial and statistical data analysis / / Israël César Lerman and Henri Leredde
| Seriation in combinatorial and statistical data analysis / / Israël César Lerman and Henri Leredde |
| Autore | Lerman Israël César |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (287 pages) |
| Disciplina | 006.312 |
| Collana | Advanced Information and Knowledge Processing |
| Soggetto topico |
Data mining - Statistical methods
Combinatorial analysis - Data processing |
| ISBN | 3-030-92694-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464537103316 |
|
Lerman Israël César
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Statistical analysis and data mining
| Statistical analysis and data mining |
| Pubbl/distr/stampa | [Hoboken, N.J.] : , : Wiley Periodical, , [2008]- |
| Descrizione fisica | 1 online resource |
| Disciplina | 006 |
| Soggetto topico | Data mining - Statistical methods |
| Soggetto genere / forma | Periodicals. |
| ISSN | 1932-1872 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996207434103316 |
| [Hoboken, N.J.] : , : Wiley Periodical, , [2008]- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Statistical analysis and data mining
| Statistical analysis and data mining |
| Pubbl/distr/stampa | [Hoboken, N.J.] : , : Wiley Periodical, , [2008]- |
| Descrizione fisica | 1 online resource |
| Disciplina | 006 |
| Soggetto topico | Data mining - Statistical methods |
| Soggetto genere / forma | Periodicals. |
| ISSN | 1932-1872 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910172096203321 |
| [Hoboken, N.J.] : , : Wiley Periodical, , [2008]- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner
| Statistical and machine-learning data mining : techniques for better predictive modeling and analysis of big data / / Bruce Ratner |
| Autore | Ratner Bruce |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Boca Raton : , : Taylor & Francis, , 2012 |
| Descrizione fisica | 1 online resource (524 p.) |
| Disciplina | 658.8/72 |
| Altri autori (Persone) | RatnerBruce |
| Soggetto topico |
Database marketing - Statistical methods
Data mining - Statistical methods |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-429-24862-8
1-4665-5121-6 1-280-12244-7 9786613526304 1-4398-6092-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Dedication; Contents; Preface; Acknowledgments; About the Author; 1. Introduction; 2. Two Basic Data Mining Methods for Variable Assessment; 3. CHAID-Based Data Mining for Paired-Variable Assessment; 4. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice; 5. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data; 6. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment; 7. The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They?
8. Logistic Regression: The Workhorse of Response Modeling9. Ordinary Regression: The Workhorse of Profit Modeling; 10. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution; 11. CHAID for Interpreting a Logistic Regression Model; 12. The Importance of the Regression Coefficient; 13. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables; 14. CHAID for Specifying a Model with Interaction Variables; 15. Market Segmentation Classification Modeling with Logistic Regression 16. CHAID as a Method for Filling in Missing Values17. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling; 18. Assessment of Marketing Models; 19. Bootstrapping in Marketing: A New Approach for Validating Models; 20. Validating the Logistic Regression Model: Try Bootstrappin; 21. Visualization of Marketing ModelsData Mining to Uncover Innards of a Model; 22. The Predictive Contribution Coefficient: A Measure of Predictive Importance; 23. Regression Modeling Involves Art, Science, and Poetry, Too; 24. Genetic and Statistic Regression Models: A Comparison 25. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model26. A Data Mining Method for Moderating Outliers Instead of Discarding Them; 27. Overfitting: Old Problem, New Solution; 28. The Importance of Straight Data: Revisited; 29. The GenIQ Model: Its Definition and an Application; 30. Finding the Best Variables for Marketing Models; 31. Interpretation of Coefficient-Free Models |
| Record Nr. | UNINA-9910457262503321 |
|
Ratner Bruce
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| Boca Raton : , : Taylor & Francis, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
