Sistemi a microprocessore / Gianni Conte ... [et al.]

• Record Nr.: UNISA990000221390203316
• Pubbl/distr/stampa: Torino : Boringhieri, copyr. 1979
• Descrizione fisica: 156 p. : ill. ; 21 cm
• Collana: Lezioni e seminari. Quaderni di elettronica ; 5
• Disciplina: 621381
• Collocazione: 621.381 SIS
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Data mining [[electronic resource] ] : concepts and techniques / / Jiawei Han, Micheline Kamber, Jian Pei

• Record Nr.: UNINA9910789437303321
• Autore: Han Jiawei
• Pubbl/distr/stampa: Burlington, Mass., : Elsevier, c2012
• ISBN: 1-283-17117-1; 9786613171177; 0-12-381480-4
• Edizione: [3rd ed.]
• Descrizione fisica: 1 recurso en línea (745 páginas)
• Collana: The Morgan Kaufmann series in data management systems
• Altri autori (Persone): KamberMicheline; PeiJian
• Disciplina: 006.3/12
• Soggetti: Data mining
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• 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
• Sommario/riassunto: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with