"Data Mining: Know It All" by Soumen Chakrabarti, Earl Cox, Eibe Frank, Ralf Hartmut G?ting, Jiawei Han, Xia Jiang, Micheline Kamber, Sam S. Lightstone, Thomas P. Nadeau, Richard E. Neapolitan, Dorian Pyle, Mamdouh Refaat, Markus Schneider, Toby J. Teorey, Ian H. Witten
Elsevier Inc., Morgan Kaufmann | 2009 | ISBN: 0123746299 | 477 pages | File type: PDF | 4 mb
This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.
It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics - from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology.
Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are comb
ined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining.
* Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints.
* Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader's technical expertise and ability to implement practical solutions.
* Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.
About This Book
CHAPTER 1 What's It All About?
1.1 Data Mining and Machine Learning
1.2 Simple Examples: The Weather Problem and Others
1.3 Fielded Applications
1.4 Machine Learning and Statistics
1.5 Generalization as Search
1.6 Data Mining and Ethics
CHAPTER 2 Data Acquisition and Integration
2.2 Sources of Data
2.3 Variable Types
2.4 Data Rollup
2.5 Rollup with Sums, Averages, and Counts
2.6 Calculation of the Mode
2.7 Data Integration
CHAPTER 3 Data Preprocessing
3.1 Why Preprocess the Data?
3.2 Descriptive Data Summarization
3.3 Data Cleaning
3.4 Data Integration and Transformation
3.5 Data Reduction
3.6 Data Discretization and Concept Hierarchy Generation
CHAPTER 4 Physical Design for Decision Support, Warehousing, and OLAP
4.1 What Is Online Analytical Processing?
4.2 Dimension Hierarchies
4.3 Star and Snowflake Schemas
4.4 Warehouses and Marts
4.5 Scaling up the System
4.6 Dss, Warehousing, and Olap Design Considerations
4.7 Usage Syntax and Examples for Major Database Servers
4.9 Literature Summary
CHAPTER 5 Algorithms: The Basic Methods
5.1 Inferring Rudimentary Rules
5.2 Statistical Modeling
5.3 Divide and Conquer: Constructing Decision Trees
5.4 Covering Algorithms: Constructing Rules
5.5 Mining Association Rules
5.6 Linear Models
5.7 Instance-based Learning
CHAPTER 6 Further Techniques in Decision Analysis
6.1 Modeling Risk Preferences
6.2 Analyzing Risk Directly
6.4 Sensitivity Analysis
6.5 Value of Information
6.6 Normative Decision Analysis
CHAPTER 7 Fundamental Concepts of Genetic Algorithms
7.1 The Vocabulary of Genetic Algorithms
7.2 Overview. 230
7.3 The Architecture of a Genetic Algorithm
7.4 Practical Issues in Using a Genetic Algorithm
CHAPTER 8 Data Structures and Algorithms for Moving Objects Types
8.1 Data Structures
8.2 Algorithms for Operations on Temporal Data Types
8.3 Algorithms for Lifted Operations
CHAPTER 9 Improving the Model
9.1 Learning from Errors
9.2 Improving Model Quality, Solving Problems
CHAPTER 10 Social Network Analysis
10.1 Social Sciences and Bibliometry
10.2 Pagerank and Hyperlink-induced Topic Search
10.3 Shortcomings of the Coarse-grained Graph Model
10.4 Enhanced Models and Techniques
10.5 Evaluation of Topic Distillati