|
| Titre : |
Data mining : concepts and techniques |
| Type de document : |
texte imprimé |
| Auteurs : |
Jiawei Han, Auteur ; Micheline Kamber, Auteur ; Jian Pei, Auteur |
| Mention d'édition : |
3th edition |
| Editeur : |
Amsterdam : Elsevier |
| Année de publication : |
2012 |
| Autre Editeur : |
Boston : Morgan Kaufmann |
| Importance : |
1 vol. (703 p.) |
| Présentation : |
ill., tabl., graph., couv. ill. en coul. |
| Format : |
24 cm |
| ISBN/ISSN/EAN : |
978-0-12-381479-1 |
| Langues : |
Anglais (eng) |
| Catégories : |
Informatique
|
| Mots-clés : |
Exploration de données
Data mining |
| Index. décimale : |
004 Informatique |
| Résumé : |
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.
This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. |
| Note de contenu : |
Table of Contents
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
2. Getting to Know Your Data
Publisher Summary
2.1 Data Objects and Attribute Types
2.2 Basic Statistical Descriptions of Data
2.3 Data Visualization
2.4 Measuring Data Similarity and Dissimilarity
2.5 Summary
2.6 Exercises
2.7 Bibliographic Notes
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
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 Implementation
4.5 Data Generalization by Attribute-Oriented Induction
4.6 Summary
4.7 Exercises
Bibliographic Notes
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
6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
Publisher Summary
6.1 Basic Concepts
6.2 Frequent Itemset Mining Methods
6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods
6.4 Summary
6.5 Exercises
6.6 Bibliographic Notes
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
8. Classification: Basic Concepts
8.1 Basic Concepts
8.2 Decision Tree Induction
8.3 Bayes Classification Methods
8.4 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
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
Summary
9.9 Exercises
9.10 Bibliographic Notes
10. Cluster Analysis: Basic Concepts and Methods
10.1 Cluster Analysis
10.2 Partitioning Methods
10.3 Hierarchical Methods
10.4 Density-Based Methods
10.5 Grid-Based Methods
10.6 Evaluation of Clustering
10.7 Summary
10.8 Exercises
10.9 Bibliographic Notes
11. Advanced Cluster Analysis
11.1 Probabilistic Model-Based Clustering
11.2 Clustering High-Dimensional Data
11.3 Clustering Graph and Network Data
11.4 Clustering with Constraints
Summary
11.6 Exercises
11.7 Bibliographic Notes
12. Outlier Detection
12.1 Outliers and Outlier Analysis
12.2 Outlier Detection Methods
12.3 Statistical Approaches
12.4 Proximity-Based Approaches
12.5 Clustering-Based Approaches
12.6 Classification-Based Approaches
12.7 Mining Contextual and Collective Outliers
12.8 Outlier Detection in High-Dimensional Data
12.9 Summary
12.10 Exercises
12.11 Bibliographic Notes
13. Data Mining Trends and Research Frontiers
13.1 Mining Complex Data Types
13.2 Other Methodologies of Data Mining
13.3 Data Mining Applications
13.4 Data Mining and Society
13.5 Data Mining Trends
13.6 Summary
13.7 Exercises
13.8 Bibliographic Notes
Bibliography
Index |
| Côte titre : |
Fs/19727 |
| En ligne : |
https://ia800702.us.archive.org/7/items/datamining_201811/DS-book%20u5.pdf |
Data mining : concepts and techniques [texte imprimé] / Jiawei Han, Auteur ; Micheline Kamber, Auteur ; Jian Pei, Auteur . - 3th edition . - Amsterdam : Elsevier : Boston : Morgan Kaufmann, 2012 . - 1 vol. (703 p.) : ill., tabl., graph., couv. ill. en coul. ; 24 cm. ISBN : 978-0-12-381479-1 Langues : Anglais ( eng)
| Catégories : |
Informatique
|
| Mots-clés : |
Exploration de données
Data mining |
| Index. décimale : |
004 Informatique |
| Résumé : |
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.
This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. |
| Note de contenu : |
Table of Contents
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
2. Getting to Know Your Data
Publisher Summary
2.1 Data Objects and Attribute Types
2.2 Basic Statistical Descriptions of Data
2.3 Data Visualization
2.4 Measuring Data Similarity and Dissimilarity
2.5 Summary
2.6 Exercises
2.7 Bibliographic Notes
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
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 Implementation
4.5 Data Generalization by Attribute-Oriented Induction
4.6 Summary
4.7 Exercises
Bibliographic Notes
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
6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
Publisher Summary
6.1 Basic Concepts
6.2 Frequent Itemset Mining Methods
6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods
6.4 Summary
6.5 Exercises
6.6 Bibliographic Notes
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
8. Classification: Basic Concepts
8.1 Basic Concepts
8.2 Decision Tree Induction
8.3 Bayes Classification Methods
8.4 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
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
Summary
9.9 Exercises
9.10 Bibliographic Notes
10. Cluster Analysis: Basic Concepts and Methods
10.1 Cluster Analysis
10.2 Partitioning Methods
10.3 Hierarchical Methods
10.4 Density-Based Methods
10.5 Grid-Based Methods
10.6 Evaluation of Clustering
10.7 Summary
10.8 Exercises
10.9 Bibliographic Notes
11. Advanced Cluster Analysis
11.1 Probabilistic Model-Based Clustering
11.2 Clustering High-Dimensional Data
11.3 Clustering Graph and Network Data
11.4 Clustering with Constraints
Summary
11.6 Exercises
11.7 Bibliographic Notes
12. Outlier Detection
12.1 Outliers and Outlier Analysis
12.2 Outlier Detection Methods
12.3 Statistical Approaches
12.4 Proximity-Based Approaches
12.5 Clustering-Based Approaches
12.6 Classification-Based Approaches
12.7 Mining Contextual and Collective Outliers
12.8 Outlier Detection in High-Dimensional Data
12.9 Summary
12.10 Exercises
12.11 Bibliographic Notes
13. Data Mining Trends and Research Frontiers
13.1 Mining Complex Data Types
13.2 Other Methodologies of Data Mining
13.3 Data Mining Applications
13.4 Data Mining and Society
13.5 Data Mining Trends
13.6 Summary
13.7 Exercises
13.8 Bibliographic Notes
Bibliography
Index |
| Côte titre : |
Fs/19727 |
| En ligne : |
https://ia800702.us.archive.org/7/items/datamining_201811/DS-book%20u5.pdf |
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