05284nam a2200289 a 4500 29808 20230531230839.0 130910s2012 us m a001 0 eng d 9780123814791 PITLIB QA76.9.D343 H233 2012 Han, Jiawei 38924 Data mining : concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei [book] 3rd ed. Burlington, MA : Elsevier, c2012. 19772 xxxii, 703 p. : ill. •v004– New Arrivals- Sep. 2013 includes index 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. Data mining 17634 0023 วิศวกรรมศาสตรบัณฑิต สาขาวิศวกรรมคอมพิวเตอร์ CPE (ป.ตรี) 65 0021 วิทยาศาสตร์บัณฑิต สาขาเทคโนโลยีสารสนเทศ IT (ป.ตรี) 64 Kamber, Micheline 38925 BK 29808 29808 29808 0 0 lcc 0 0 BKE PIMLIB PIMLIB GEN-EN 2013-09-12 Asia Books 2355.00 0130000043243 0 QA76.9.D343 H233 2012 32550000225626 2023-05-09 2355.00 2023-05-09 BK 0 0 lcc 0 0 BKE PIMLIB PIMLIB GEN-EN 2014-07-16 Asia Books 3600.00 0130000042393 3 QA76.9.D343 H233 2012 32550000229641 2023-05-09 c. 2 3600.00 2023-05-09 BK 0 0 lcc 0 0 BKE PIMLIB PIMLIB GEN-EN 2014-07-16 Asia Books 3600.00 0130000042394 1 2 QA76.9.D343 H233 2012 32550000229642 2023-05-09 c. 3 3600.00 2023-05-09 BK