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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, itOCOs 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 editionOCOs 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 todayOCOs 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" 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. 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. Front Cover -- Data Mining Concepts and Techniques -- Copyright Page -- Table of Contents -- Dedication -- 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 Data -- 2.3 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 Implementation -- 4.5 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.
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