Data Mining: Concepts and Techniques | Guide books

Data Mining: Concepts and TechniquesJuly 2011

July 2011

Publisher:

Morgan Kaufmann Publishers Inc.
340 Pine Street, Sixth Floor
San Francisco
CA
United States

ISBN:978-0-12-381479-1

Published:29 July 2011

Pages:

696

Available at Amazon

Bibliometrics

Abstract

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 real business data

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J. Han
University of Illinois Urbana-Champaign
- Publication Years1986 - 2024
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Simon Fraser University
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Simon Fraser University
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Data Mining: Concepts and Techniques
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Reviewer: Robert M. Lynch

A very good textbook on data mining, this third edition reflects the changes that are occurring in the data mining field. It adds cited material from about 2006, a new section on visualization, and pattern mining with the more recent cluster methods. It's a well-written text, with all of the supporting materials an instructor is likely to want, including Web material support, extensive problem sets, and solution manuals. Though it serves as a data mining text, readers with little experience in the area will find it readable and enlightening. That being said, readers are expected to have some coding experience, as well as database design and statistics analysis knowledge. The material is organized into 13 chapters, with chapters 1 through 3 serving as an introduction to mining, data organization (objects and attributes), statistics, data visualization, and processing. Chapter 4 focuses on data warehousing, and chapter 5 examines data cube technology. Chapters 6 through 13 attend to data mining concepts and methods. These chapters focus on mining patterns and associations, advanced patterns, concept classification, and model evaluation with advanced methods; cluster analysis and outlier detection are discussed at length. Lastly, mining trends and recent research opportunities are presented. Two additional items are worthy of note: the text's bibliography is an excellent reference list for mining research, and the index is very complete, which makes it easy to locate information. Also, researchers and analysts from other disciplines"?for example, epidemiologists, financial analysts, and psychometric researchers"?may find the material very useful. Online Computing Reviews Service

Reviewer: Sergio Ilarri

This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining--the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers. Chapter 1, "Introduction," provides a general overview of data mining and related technologies. Chapter 2, "Getting to Know Your Data," presents different types of attributes, statistical descriptions of data, visualization techniques, and similarity and dissimilarity measures. Chapter 3, "Data Preprocessing," presents different techniques for data cleaning, data integration, data reduction, data transformation, and data discretization. Chapter 4, "Data Warehousing and Online Analytical Processing," introduces data warehouses and OLAP. Chapter 5, "Data Cube Technology," examines data cube technology and methods to compute data cubes. Chapter 6, "Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods," covers basic aspects of frequent patterns, associations, and correlations, as well as pattern evaluation measures. Chapter 7, "Advanced Pattern Mining," describes advanced methods for mining different types of patterns (multilevel, multidimensional, rare, colossal, and approximate). It also considers the problem of semantic annotation of frequent patterns and applications of pattern mining. Chapter 8, "Classification: Basic Concepts," presents basic classification methods (decision trees, Bayesian classifiers, and rule-based classifiers), evaluation metrics, and ensemble methods used to increase the accuracy of the classification. Chapter 9, "Classification: Advanced Methods," describes advanced techniques for classification: Bayesian belief networks, backpropagation, support vector machines, classification using frequent patterns, and lazy/instance-based learners (based on k -nearest neighbors and case-based reasoning). This chapter also offers a brief description of other approaches (based on genetic algorithms and rough sets) and discusses multiclass classification, semi-supervised classification, active learning, and transfer learning. Chapter 10, "Cluster Analysis: Basic Concepts and Methods," introduces the problem of grouping objects into subsets, providing an overview of basic clustering methods (partitioning, hierarchical, density-based, and grid-based methods) and cluster evaluation. Chapter 11, "Advanced Cluster Analysis," discusses probabilistic methods, high-dimensional data (data with ten or more attributes), graph and network data, and clustering constraints. Chapter 12, "Outlier Detection," studies techniques for the detection of anomalies, including statistical, proximity-based, clustering-based, and classification-based approaches. In addition, this chapter describes the problem of contextual/conditional and collective outliers, and how to detect outliers in high-dimensional data. Finally, chapter 13, "Data Mining Trends and Research Frontiers," introduces the problem of mining complex data types (for example, biological sequences, graphs and networks, spatiotemporal data, and Web data), other methodologies for data mining not covered in the book, data mining applications, the impact of data mining on society, and open research issues. Researchers in data mining or related areas could use this book as a reference. It could also be used as a textbook in a data mining course. Each chapter ends with a useful summary, some exercises, and bibliographic notes. Moreover, the book's Web site (http://www.cs.uiuc.edu/~hanj/bk3/) provides additional resources for instructors. I must emphasize that the book focuses on concepts and techniques; thus, it does not cover the use of specific data mining software tools. In addition, the authors have decided to "leave the handling of complex data types to a separate forthcoming book." Online Computing Reviews Service

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