Data Mining, Southeast Asia Edition

Front Cover
Elsevier, 2006 M04 6 - 800 pages
23 Reviews

Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.

Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data.

  • A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data
  • Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning
  • Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects
  • Complete classroom support for instructors at www.mkp.com/datamining2e companion site

From inside the book

 

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Page 47
Data reductioncan reduce the data size by aggregating, eliminating redundant
features, or clustering, for instance. ... The methods for data preprocessing are
organized into the following categories: data cleaning (Section 2.3), data
integration ...
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This is a good book. Lot of thinking work is needed to read such books. As they say....its all in your head.

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Selected pages

Contents

1 Introduction
1
2 Data Preprocessing
47
An Overview
105
4 Data Cube Computation and Data Generalization
157
5 Mining Frequent Patterns Associations and Correlations
227
6 Classification and Prediction
285
7 Cluster Analysis
383
8 Mining Stream TimeSeries and Sequence Data
467
9 Graph Mining Social Network Analysis and Multirelational Data Mining
535
10 Mining Object Spatial Multimedia Text and Web Data
591
11 Applications and Trends in Data Mining
649
An Introduction to Microsofts OLE DB for Data Mining
691
Bibliography
703
Copyright

Other editions - View all

Data Mining: Concepts and Techniques
Jiawei Han
No preview available - 2006

Common terms and phrases

accuracy aggregate algorithm AllElectronics applications approach Apriori association rules attribute Bayesian categorical cells Chapter class label classification cluster analysis clustering methods concept hierarchy constraints contains correlation count crosstab cuboid data analysis data cube data marts data mining system data set data streams data warehouse database systems decision tree defined described dimension table dimensions distribution document efficient example Figure frequent itemsets frequent patterns function given graph graph mining iceberg cube input k-medoids machine learning measure minimum support mining process multidimensional multimedia data multiple multirelational neural network node objects OLAP on-line outliers partition pattern mining performed prediction pruning regression relational database represented retrieval ROLAP scalable schema Section selection sequence similar snowflake schema space spatial data specified statistical stored stream data structure subset Suppose target techniques tion training tuples transaction transformation tuples typically values variables vector visualization

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References to this book

About the author (2006)

Jiawei Han is Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has received many awards for his contributions in the field, including the 2004 ACM SIGKDD Innovations Award. He has served as Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data, and on editorial boards of several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery.

Jian Pei is currently a Canada Research Chair (Tier 1) in Big Data Science and a Professor in the School of Computing Science at Simon Fraser University. He is also an associate member of the Department of Statistics and Actuarial Science. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is recognized as a Fellow of the Association of Computing Machinery (ACM) for his “contributions to the foundation, methodology and applications of data mining and as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for his “contributions to data mining and knowledge discovery . He is the editor-in-chief of the IEEE Transactions of Knowledge and Data Engineering (TKDE), a director of the Special Interest Group on Knowledge Discovery in Data (SIGKDD) of the Association for Computing Machinery (ACM), and a general co-chair or program committee co-chair of many premier conferences.

Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. She has a master's degree in computer science (specializing in artificial intelligence) from Concordia University, Canada.

Bibliographic information

QR code for Data Mining, Southeast Asia Edition
TitleData Mining, Southeast Asia Edition
The Morgan Kaufmann Series in Data Management Systems
AuthorsJiawei Han, Jian Pei, Micheline Kamber
Edition2
PublisherElsevier, 2006
ISBN0080475582, 9780080475585
Length800 pages
  
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