Han, Jiawei <br/><br/>
Data mining :  concepts and techniques /  [book] 
Jiawei Han, Micheline Kamber, Jian Pei 
 - 3rd ed. 
 - Burlington, MA :  Elsevier,  c2012. 
 - xxxii, 703 p. :  ill. 
<br/><br/>includes index 
<br/><br/>Chapter 1. Introduction --  Index.  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--  Chapter 2. 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--  Chapter --  3. Data Preprocessing--  --  3.1 Data Preprocessing: An Overview--  3.2 Data Cleaning--  3.3 Data Integration--  3.4 Data Reduction--  3.5 Data Transformation and Data iscretization--  3.6 Summary--  3.7 Exercises--  3.8 Bibliographic Notes--  Chapter --  4. 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--  Chapter 5. 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--  Chapter 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods--  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--  Chapter t 7. 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--  Chapter 8. Classification: Basic Concepts--  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--  Chapter 9. Classification: Advanced Methods--  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--  Chapter 10. Cluster Analysis: Basic Concepts and Methods--  10.1 Cluster Analysis--  10.2 Partitioning Methods--  10.t3 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--  Chapter 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--  Chapter 12. Outlier Detection--  12.1 Outliers and Outlier Analysis--  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 Exercises12.11 Bibliographic Notes--  Chapter 13. Data Mining Trends and Research Frontiers--  13.1 Mining Complex Data Types--  13.2 Other Methodologies of Data Mining--  1--  3.3 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. 
<br/><br/><label>ISBN: </label>9780123814791 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Data mining
<br/><br/><label>LC Class. No.: </label>QA76.9.D343  / H233 2012