Data Mining: Concepts and Techniques / Edition 3

Data Mining: Concepts and Techniques / Edition 3

5.0 1
by Jiawei Han, Micheline Kamber, Jian Pei
     
 

ISBN-10: 0123814790

ISBN-13: 9780123814791

Pub. Date: 07/06/2011

Publisher: Elsevier Science

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

…  See more details below

Overview

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 thisThird Editionof 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.

Read More

Product Details

ISBN-13:
9780123814791
Publisher:
Elsevier Science
Publication date:
07/06/2011
Series:
Morgan Kaufmann Series in Data Management Systems Series
Pages:
744
Product dimensions:
7.80(w) x 9.30(h) x 1.70(d)

Table of Contents

1: Introduction
2: Data Warehouse and OLAP Technology for DataMining
3: Data Preparation
4: Data Mining Primitives, Languages, and System Architectures
5: Concept Description: Characterization and Comparison
6: Mining Association Rules in Large Databases
7: Classification and Prediction
8: Cluster Analysis
9: Mining Complex Types of Data
10: Data Mining Applications and Trends in Data Mining
Appendix A: An Introduction to Microsoft's OLE DB for Data Mining
Appendix B: An Introduction to DBMiner
Bibliography

Customer Reviews

Average Review:

Write a Review

and post it to your social network

     

Most Helpful Customer Reviews

See all customer reviews >

Data Mining: Concepts and Techniques 5 out of 5 based on 0 ratings. 1 reviews.
FRINGEINDEPENEDENTREVIEW More than 1 year ago
Are you a computer science student, application developer, and business professional; as well as, a researcher? If you are, then this book is for you! Authors Jiawei Han, Micheline Kamber, and Jian Pei, have done an outstanding job of writing a third edition of a book which explores the concepts and techniques of knowledge discovery and data mining. Han, Kamber and Pei, begin by providing an introduction to the multidisciplinary field of data mining. In addition, the authors introduce the general data features. The authors then focus on the techniques for data processing. Then, they look at the basic concepts, modeling, design architectures, and general implementations of data warehouses and OLAP; as well as, the relationship between data warehousing and other data generalization methods. Next, the authors take an in-depth look at cube technology, presenting a detailed study of methods of data cube computation, including Star-Cubing and high-dimensional OLAP methods. They continue with an in-depth look at the fundamental concepts, such as market basket analysis, with many techniques for frequent itemset mining presented in an organized way. In addition, the authors discuss methods for pattern mining in multilevel and multidimensional space, mining rare and negative patterns, mining colossal patterns and high-dimensional data, constraint-based pattern mining, and mining compressed or approximate patterns. The authors then introduce the basic concepts and methods for classification, including decision tree induction, Bayes classification, and rule-based classification. Then, they discuss advanced methods for classification, including Bayesian belief networks, the neural network technique of backpropagation , support vector machines, classification using frequent patterns, k-nearest-neighbor classifiers, case-based reasoning, genetic algorithms, rough set theory, and fuzzy set approaches. Next, the authors introduce the basic concepts and methods for data clustering, including an overview of basic cluster analysis methods, partitioning methods, hierarchical methods, density-based methods, and grid-based methods. They continue with a discussion of advanced methods for clustering, including probabilistic model-based clustering, clustering high-dimensional data, clustering graph and network data, and clustering with constraints. In addition, the authors introduce the basic concepts of outliers and outlier analysis, and discuss various outlier detection methods from the view of degree of supervision; as well as, from the view of approaches. Finally, the authors discuss trends, applications, and research frontiers in data mining. This most excellent book is not intended as an introduction to statistics, machine learning, database systems, or other such areas. Rather, the book is a comprehensive introduction to data mining.