Data Mining: Concepts and Techniques

Klappentext:

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 an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges.
Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Inhaltsverzeichnis:

Chapter 1. Introduction
Chapter 2. Getting to Know Your Data
Chapter 3. Preprocessing
Chapter 4. Data Warehousing and On-Line Analytical Processing
Chapter 5. Data Cube Technology
Chapter 6. Mining Frequent Patterns, Associations and Correlations: Concepts and Methods
Chapter 7. Advanced Frequent Pattern Mining
Chapter 8. Classification: Basic Concepts
Chapter 9. Classification: Advanced Methods
Chapter 10. Cluster Analysis: Basic Concepts and Methods
Chapter 11. Advanced Cluster Analysis
Chapter 12. Outlier Analysis
Chapter 13. Data Mining Trends and Research Frontiers in Data Mining

Rezension:

We are living in the data deluge age. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. Thise 3rd editionThird Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The bookIt also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. The book, with its companion website, would make a great textbook for analytics, data mining, and knowledge discovery courses.-- Gregory Piatetsky, President, KDnuggets
Jiawei, Micheline, and Jian give an encyclopaedic coverage of all the related methods, from the classic topics of clustering and classification, to database methods (association rules, data cubes) to more recent and advanced topics (SVD/PCA , wavelets, support vector machines).. Overall, it is an excellent book on classic and modern data mining methods alike, and it is ideal not only for teaching, but as a reference book.- From the foreword by Christos Faloutsos, Carnegie Mellon University

Themen:

• Data Mining

Verwandte Themen:

• Business Intelligence
• Information Retrieval
• Datenanalyse
• Künstliche Intelligenz
• Maschinelles Lernen
• Softwareentwicklung
• Wirtschaftsinformatik
• Data Warehouse
• OLAP (Online Analytical Processing)
• Microsoft SQL Server 2005