DATA MINING Concepts and Techniques, 3rd Ed (2012) pdf RUPOM

DATA MINING Concepts and Techniques, 3rd Ed (2012)

TRUE PDF WITH BOOKMARKS & INDEX


Authors:
Jiawei Han
Micheline Kamber
Jian Pei
Imprint: Elsevier
Publisher: Morgan Kaufmann
Language : English
Pages: 517
Reader Required: Adobe Reader, Foxit, Nitro etc.
Published : July 6, 2011
ISBN: 978-0123814791


Contents:

01. 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

02. 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

03. Data Processing
3.1 Data Preprocessing: An Overview
3.2 Data Cleaning
3.3 Data Integration
3.4 Data Reduction
3.5 Data Transformation & Data Discretization
3.6 Summary
3.7 Exercises
3.8 Bibliographic Notes

04. 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

05. 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

06. Mining Frequent Patterns, Associations, and Correlations
6.1 Basic Concepts
6.2 Frequent Itemset Mining Methods
6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods
6.4 Summary
6.5 Exercises
6.6 Bibliographic Notes

07. Advanced Pattern Mining
7.1 Pattern Mining: A Road Map
7.2 Pattern Mining in Multilevel, Multidimensional Space
7.3 Constraint-Based Frequent Pattern Mining
7.4 Mining High-Dimensional Data and Colossal Patterns
7.5 Mining Compressed or Approximate Patterns
7.6 Pattern Exploration and Application
7.7 Summary
7.8 Exercises
7.9 Bibliographic Notes

08. Classification
8.1 Basic Concepts
8.2 Decision Tree Induction
8.3 Bayes Classification Methods
8.4 Rule-Based Classification
8.5 Model Evaluation and Selection
8.6 Techniques to Improve Classification Accuracy
8.7 Summary
8.8 Exercises
8.9 Bibliographic Notes

09. Classification
9.1 Bayesian Belief Networks
9.2 Classification by Backpropagation
9.3 Support Vector Machines
9.4 Classification Using Frequent Patterns
9.5 Lazy Learners (or Learning from Your Neighbors)
9.6 Other Classification Methods
9.7 Additional Topics Regarding Classification
9.8 Summary
9.9 Exercises
9.10 Bibliographic Notes

10. Cluster Analysis
10.1 Cluster Analysis
10.2 Partitioning Methods
10.3 Hierarchical Methods
10.4 Density-Based Methods
10.5 Grid-Based Methods
10.6 Evaluation of Clustering
10.7 Summary
10.8 Exercises
10.9 Bibliographic Notes

11. Advanced Cluster Analysis
11.1 Probabilistic Model-Based Clustering
11.2 Clustering High-Dimensional Data
11.3 Clustering Graph and Network Data
11.4 Clustering with Constraints
11.5 Summary
11.6 Exercises
11.7 Bibliographic Notes

12. Outlier Detection
12.1 Outliers and Outlier Analysis
12.2 Outlier Detection Methods
12.2 Outlier Detection Methods
12.3 Statistical Approaches
12.4 Proximity-Based Approaches
12.5 Clustering-Based Approaches
12.6 Classification-Based Approaches
12.7 Mining Contextual and Collective Outliers
12.8 Outlier Detection in High-Dimensional Data
12.9 Summary
12.10 Exercises
12.11 Bibliographic Notes

13. Data Mining Trends and Research Frontiers
13.1 Mining Complex Data Types
13.2 Other Methodologies of Data Mining
13.3 Data Mining Applications
13.4 Data Mining and Society
13.5 Data Mining Trends
13.6 Summary
13.7 Exercises
13.8 Bibliographic Notes

Bibliography
Index