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Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber, Jian Pei
| Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber, Jian Pei |
| Autore | Han Jiawei |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Burlington, Mass., : Elsevier, c2012 |
| Descrizione fisica | 1 recurso en línea (745 páginas) |
| Disciplina | 006.3/12 |
| Altri autori (Persone) |
KamberMicheline
PeiJian |
| Collana | The Morgan Kaufmann series in data management systems |
| Soggetto topico | Data mining |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-283-17117-1
9786613171177 0-12-381480-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Data Mining: Concepts and Techniques; Copyright; Dedication; Table of Contents; Foreword; Foreword to Second Edition; Preface; Acknowledgments; About the Authors; Chapter 1. Introduction; 1.1 Why Data Mining?; 1.2 What Is Data Mining?; 1.3 What Kinds of Data Can Be Mined?; 1.4 What Kinds of Patterns Can Be Mined?; 1.5 Which Technologies Are Used?; 1.6 Which Kinds of Applications Are Targeted?; 1.7 Major Issues in Data Mining; 1.8 Summary; 1.9 Exercises; 1.10 Bibliographic Notes; Chapter 2. Getting to Know Your Data; 2.1 Data Objects and Attribute Types
2.2 Basic Statistical Descriptions of Data2.3 Data Visualization; 2.4 Measuring Data Similarity and Dissimilarity; 2.5 Summary; 2.6 Exercises; 2.7 Bibliographic Notes; Chapter 3. Data Preprocessing; 3.1 Data Preprocessing: An Overview; 3.2 Data Cleaning; 3.3 Data Integration; 3.4 Data Reduction; 3.5 Data Transformation and Data Discretization; 3.6 Summary; 3.7 Exercises; 3.8 Bibliographic Notes; Chapter 4. Data Warehousing and Online Analytical Processing; 4.1 Data Warehouse: Basic Concepts; 4.2 Data Warehouse Modeling: Data Cube and OLAP; 4.3 Data Warehouse Design and Usage 4.4 Data Warehouse Implementation4.5 Data Generalization by Attribute-Oriented Induction; 4.6 Summary; 4.7 Exercises; 4.8 Bibliographic Notes; Chapter 5. Data Cube Technology; 5.1 Data Cube Computation: Preliminary Concepts; 5.2 Data Cube Computation Methods; 5.3 Processing Advanced Kinds of Queries by Exploring Cube Technology; 5.4 Multidimensional Data Analysis in Cube Space; 5.5 Summary; 5.6 Exercises; 5.7 Bibliographic Notes; Chapter 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods; 6.1 Basic Concepts; 6.2 Frequent Itemset Mining Methods 6.3 Which Patterns Are Interesting?-Pattern Evaluation Methods6.4 Summary; 6.5 Exercises; 6.6 Bibliographic Notes; Chapter 7. Advanced Pattern Mining; 7.1 Pattern Mining: A Road Map; 7.2 Pattern Mining in Multilevel, Multidimensional Space; 7.3 Constraint-Based Frequent Pattern Mining; 7.4 Mining High-Dimensional Data and Colossal Patterns; 7.5 Mining Compressed or Approximate Patterns; 7.6 Pattern Exploration and Application; 7.7 Summary; 7.8 Exercises; 7.9 Bibliographic Notes; Chapter 8. Classification: Basic Concepts; 8.1 Basic Concepts; 8.2 Decision Tree Induction 8.3 Bayes Classification Methods8.4 Rule-Based Classification; 8.5 Model Evaluation and Selection; 8.6 Techniques to Improve Classification Accuracy; 8.7 Summary; 8.8 Exercises; 8.9 Bibliographic Notes; Chapter 9. Classification: Advanced Methods; 9.1 Bayesian Belief Networks; 9.2 Classification by Backpropagation; 9.3 Support Vector Machines; 9.4 Classification Using Frequent Patterns; 9.5 Lazy Learners (or Learning from Your Neighbors); 9.6 Other Classification Methods; 9.7 Additional Topics Regarding Classification; 9.8 Summary; 9.9 Exercises; 9.10 Bibliographic Notes Chapter 10. Cluster Analysis: Basic Concepts and Methods |
| Record Nr. | UNINA-9910461363903321 |
Han Jiawei
|
||
| Burlington, Mass., : Elsevier, c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber, Jian Pei
| Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber, Jian Pei |
| Autore | Han Jiawei |
| Edizione | [3rd ed.] |
| Pubbl/distr/stampa | Burlington, Mass., : Elsevier, c2012 |
| Descrizione fisica | 1 recurso en línea (745 páginas) |
| Disciplina | 006.3/12 |
| Altri autori (Persone) |
KamberMicheline
PeiJian |
| Collana | The Morgan Kaufmann series in data management systems |
| Soggetto topico | Data mining |
| ISBN |
1-283-17117-1
9786613171177 0-12-381480-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Data Mining: Concepts and Techniques; Copyright; Dedication; Table of Contents; Foreword; Foreword to Second Edition; Preface; Acknowledgments; About the Authors; Chapter 1. Introduction; 1.1 Why Data Mining?; 1.2 What Is Data Mining?; 1.3 What Kinds of Data Can Be Mined?; 1.4 What Kinds of Patterns Can Be Mined?; 1.5 Which Technologies Are Used?; 1.6 Which Kinds of Applications Are Targeted?; 1.7 Major Issues in Data Mining; 1.8 Summary; 1.9 Exercises; 1.10 Bibliographic Notes; Chapter 2. Getting to Know Your Data; 2.1 Data Objects and Attribute Types
2.2 Basic Statistical Descriptions of Data2.3 Data Visualization; 2.4 Measuring Data Similarity and Dissimilarity; 2.5 Summary; 2.6 Exercises; 2.7 Bibliographic Notes; Chapter 3. Data Preprocessing; 3.1 Data Preprocessing: An Overview; 3.2 Data Cleaning; 3.3 Data Integration; 3.4 Data Reduction; 3.5 Data Transformation and Data Discretization; 3.6 Summary; 3.7 Exercises; 3.8 Bibliographic Notes; Chapter 4. Data Warehousing and Online Analytical Processing; 4.1 Data Warehouse: Basic Concepts; 4.2 Data Warehouse Modeling: Data Cube and OLAP; 4.3 Data Warehouse Design and Usage 4.4 Data Warehouse Implementation4.5 Data Generalization by Attribute-Oriented Induction; 4.6 Summary; 4.7 Exercises; 4.8 Bibliographic Notes; Chapter 5. Data Cube Technology; 5.1 Data Cube Computation: Preliminary Concepts; 5.2 Data Cube Computation Methods; 5.3 Processing Advanced Kinds of Queries by Exploring Cube Technology; 5.4 Multidimensional Data Analysis in Cube Space; 5.5 Summary; 5.6 Exercises; 5.7 Bibliographic Notes; Chapter 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods; 6.1 Basic Concepts; 6.2 Frequent Itemset Mining Methods 6.3 Which Patterns Are Interesting?-Pattern Evaluation Methods6.4 Summary; 6.5 Exercises; 6.6 Bibliographic Notes; Chapter 7. Advanced Pattern Mining; 7.1 Pattern Mining: A Road Map; 7.2 Pattern Mining in Multilevel, Multidimensional Space; 7.3 Constraint-Based Frequent Pattern Mining; 7.4 Mining High-Dimensional Data and Colossal Patterns; 7.5 Mining Compressed or Approximate Patterns; 7.6 Pattern Exploration and Application; 7.7 Summary; 7.8 Exercises; 7.9 Bibliographic Notes; Chapter 8. Classification: Basic Concepts; 8.1 Basic Concepts; 8.2 Decision Tree Induction 8.3 Bayes Classification Methods8.4 Rule-Based Classification; 8.5 Model Evaluation and Selection; 8.6 Techniques to Improve Classification Accuracy; 8.7 Summary; 8.8 Exercises; 8.9 Bibliographic Notes; Chapter 9. Classification: Advanced Methods; 9.1 Bayesian Belief Networks; 9.2 Classification by Backpropagation; 9.3 Support Vector Machines; 9.4 Classification Using Frequent Patterns; 9.5 Lazy Learners (or Learning from Your Neighbors); 9.6 Other Classification Methods; 9.7 Additional Topics Regarding Classification; 9.8 Summary; 9.9 Exercises; 9.10 Bibliographic Notes Chapter 10. Cluster Analysis: Basic Concepts and Methods |
| Record Nr. | UNINA-9910789437303321 |
|
Han Jiawei
|
||
| Burlington, Mass., : Elsevier, c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber
| Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber |
| Autore | Han Jiawei |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; London, : Elsevier, c2006 |
| Descrizione fisica | 1 online resource (772 p.) |
| Disciplina | 005.741 |
| Altri autori (Persone) | KamberMicheline |
| Collana | The Morgan Kaufmann series in data management systems |
| Soggetto topico |
Data mining
Computer science |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-66586-3
9786612665868 0-08-047558-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front cover; Title page; Copyright page; Dedication; Table of contents; Foreword; Preface; Organization of the Book; To the Instructor; To the Student; To the Professional; Book Websites with Resources; Acknowledgments for the First Edition of the Book; Acknowledgments for the Second Edition of the Book; 1 Introduction; 1.1 What Motivated Data Mining? Why Is It Important?; 1.2 So, What Is Data Mining?; 1.3 Data Mining-On What Kind of Data?; 1.4 Data Mining Functionalities-What Kinds of Patterns Can Be Mined?; 1.5 Are All of the Patterns Interesting?; 1.6 Classification of Data Mining Systems
1.7 Data Mining Task Primitives1.8 Integration of a Data Mining System with a Database or Data Warehouse System; 1.9 Major Issues in Data Mining; 1.10 Summary; Exercises; Bibliographic Notes; 2 Data Preprocessing; 2.1 Why Preprocess the Data?; 2.2 Descriptive Data Summarization; 2.3 Data Cleaning; 2.4 Data Integration and Transformation; 2.5 Data Reduction; 2.6 Data Discretization and Concept Hierarchy Generation; 2.7 Summary; Exercises; Bibliographic Notes; 3 Data Warehouse and OLAP Technology: An Overview; 3.1 What Is a Data Warehouse?; 3.2 A Multidimensional Data Model 3.3 Data Warehouse Architecture3.4 Data Warehouse Implementation; 3.5 From Data Warehousing to Data Mining; 3.6 Summary; Exercises; Bibliographic Notes; 4 Data Cube Computation and Data Generalization; 4.1 Efficient Methods for Data Cube Computation; 4.2 Further Development of Data Cube and OLAP Technology; 4.3 Attribute-Oriented Induction-An Alternative Method for Data Generalization and Concept Description; 4.4 Summary; Exercises; Bibliographic Notes; 5 Mining Frequent Patterns, Associations, and Correlations; 5.1 Basic Concepts and a Road Map 5.2 Efficient and Scalable Frequent Itemset Mining Methods5.3 Mining Various Kinds of Association Rules; 5.4 From Association Mining to Correlation Analysis; 5.5 Constraint-Based Association Mining; 5.6 Summary; Exercises; Bibliographic Notes; 6 Classification and Prediction; 6.1 What Is Classification? What Is Prediction?; 6.2 Issues Regarding Classification and Prediction; 6.3 Classification by Decision Tree Induction; 6.4 Bayesian Classification; 6.5 Rule-Based Classification; 6.6 Classification by Backpropagation; 6.7 Support Vector Machines 6.8 Associative Classification: Classification by Association Rule Analysis6.9 Lazy Learners (or Learning from Your Neighbors); 6.10 Other Classification Methods; 6.11 Prediction; 6.12 Accuracy and Error Measures; 6.13 Evaluating the Accuracy of a Classifier or Predictor; 6.14 Ensemble Methods-Increasing the Accuracy; 6.15 Model Selection; 6.16 Summary; Exercises; Bibliographic Notes; 7 Cluster Analysis; 7.1 What Is Cluster Analysis?; 7.2 Types of Data in Cluster Analysis; 7.3 A Categorization of Major Clustering Methods; 7.4 Partitioning Methods; 7.5 Hierarchical Methods 7.6 Density-Based Methods |
| Record Nr. | UNINA-9910451443203321 |
|
Han Jiawei
|
||
| Amsterdam ; ; London, : Elsevier, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber
| Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber |
| Autore | Han Jiawei |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Amsterdam ; ; London, : Elsevier, c2006 |
| Descrizione fisica | 1 online resource (772 p.) |
| Disciplina | 005.741 |
| Altri autori (Persone) | KamberMicheline |
| Collana | The Morgan Kaufmann series in data management systems |
| Soggetto topico |
Data mining
Computer science |
| ISBN | 9780080475585 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front cover; Title page; Copyright page; Dedication; Table of contents; Foreword; Preface; Organization of the Book; To the Instructor; To the Student; To the Professional; Book Websites with Resources; Acknowledgments for the First Edition of the Book; Acknowledgments for the Second Edition of the Book; 1 Introduction; 1.1 What Motivated Data Mining? Why Is It Important?; 1.2 So, What Is Data Mining?; 1.3 Data Mining-On What Kind of Data?; 1.4 Data Mining Functionalities-What Kinds of Patterns Can Be Mined?; 1.5 Are All of the Patterns Interesting?; 1.6 Classification of Data Mining Systems
1.7 Data Mining Task Primitives1.8 Integration of a Data Mining System with a Database or Data Warehouse System; 1.9 Major Issues in Data Mining; 1.10 Summary; Exercises; Bibliographic Notes; 2 Data Preprocessing; 2.1 Why Preprocess the Data?; 2.2 Descriptive Data Summarization; 2.3 Data Cleaning; 2.4 Data Integration and Transformation; 2.5 Data Reduction; 2.6 Data Discretization and Concept Hierarchy Generation; 2.7 Summary; Exercises; Bibliographic Notes; 3 Data Warehouse and OLAP Technology: An Overview; 3.1 What Is a Data Warehouse?; 3.2 A Multidimensional Data Model 3.3 Data Warehouse Architecture3.4 Data Warehouse Implementation; 3.5 From Data Warehousing to Data Mining; 3.6 Summary; Exercises; Bibliographic Notes; 4 Data Cube Computation and Data Generalization; 4.1 Efficient Methods for Data Cube Computation; 4.2 Further Development of Data Cube and OLAP Technology; 4.3 Attribute-Oriented Induction-An Alternative Method for Data Generalization and Concept Description; 4.4 Summary; Exercises; Bibliographic Notes; 5 Mining Frequent Patterns, Associations, and Correlations; 5.1 Basic Concepts and a Road Map 5.2 Efficient and Scalable Frequent Itemset Mining Methods5.3 Mining Various Kinds of Association Rules; 5.4 From Association Mining to Correlation Analysis; 5.5 Constraint-Based Association Mining; 5.6 Summary; Exercises; Bibliographic Notes; 6 Classification and Prediction; 6.1 What Is Classification? What Is Prediction?; 6.2 Issues Regarding Classification and Prediction; 6.3 Classification by Decision Tree Induction; 6.4 Bayesian Classification; 6.5 Rule-Based Classification; 6.6 Classification by Backpropagation; 6.7 Support Vector Machines 6.8 Associative Classification: Classification by Association Rule Analysis6.9 Lazy Learners (or Learning from Your Neighbors); 6.10 Other Classification Methods; 6.11 Prediction; 6.12 Accuracy and Error Measures; 6.13 Evaluating the Accuracy of a Classifier or Predictor; 6.14 Ensemble Methods-Increasing the Accuracy; 6.15 Model Selection; 6.16 Summary; Exercises; Bibliographic Notes; 7 Cluster Analysis; 7.1 What Is Cluster Analysis?; 7.2 Types of Data in Cluster Analysis; 7.3 A Categorization of Major Clustering Methods; 7.4 Partitioning Methods; 7.5 Hierarchical Methods 7.6 Density-Based Methods |
| Record Nr. | UNINA-9910784150603321 |
|
Han Jiawei
|
||
| Amsterdam ; ; London, : Elsevier, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
RIDE-SDMA 2005: 15th International Workshop on Research Issues in Data Engineering (03-07 April 2005/Tokyo, Japan)
| RIDE-SDMA 2005: 15th International Workshop on Research Issues in Data Engineering (03-07 April 2005/Tokyo, Japan) |
| Autore | Han Jiawei |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE Computer Society Press, 2005 |
| ISBN | 1-5386-0334-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996206219903316 |
|
Han Jiawei
|
||
| [Place of publication not identified], : IEEE Computer Society Press, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
RIDE-SDMA 2005: 15th International Workshop on Research Issues in Data Engineering (03-07 April 2005/Tokyo, Japan)
| RIDE-SDMA 2005: 15th International Workshop on Research Issues in Data Engineering (03-07 April 2005/Tokyo, Japan) |
| Autore | Han Jiawei |
| Pubbl/distr/stampa | [Place of publication not identified], : IEEE Computer Society Press, 2005 |
| ISBN |
9781538603345
1538603349 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910142338103321 |
|
Han Jiawei
|
||
| [Place of publication not identified], : IEEE Computer Society Press, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Tutorial notes of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
| Tutorial notes of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Autore | Han Jiawei |
| Pubbl/distr/stampa | [Place of publication not identified], : ACM, 1999 |
| Descrizione fisica | 1 online resource (291 pages) |
| Collana | ACM Conferences |
| Soggetto topico |
Engineering & Applied Sciences
Computer Science |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Tutorial notes of the fifth Association for Computing Machinery Special Interest Group on Knowledge Discovery & Data Mining International Conference on Knowledge Discovery and Data Mining
KDD '99 the first annual International Conference on Knowledge Discovery in Data, San Diego, CA, USA - August 15 - 18, 1999 |
| Record Nr. | UNINA-9910376221503321 |
|
Han Jiawei
|
||
| [Place of publication not identified], : ACM, 1999 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||