000			05284nam a2200289 a 4500
001			29808
005			20230423231329.0
008			130910s2012 us m a001 0 eng d
020			_a9780123814791
049			_bPITLIB
050		4	_aQA76.9.D343 _bH233 2012
100	1		_aHan, Jiawei _938924
245	1	0	_aData mining : _bconcepts and techniques / _cJiawei Han, Micheline Kamber, Jian Pei _h[book]
250			_a3rd ed.
260			_aBurlington, MA : _bElsevier, _cc2012. _919772
300			_axxxii, 703 p. : _bill.
449		0	_a•v004– New Arrivals- Sep. 2013
504			_aincludes index
505		0	_aChapter 1. Introduction -- _tIndex. _t1.2 What Is Data Mining? -- _t1.-- _t3 What Kinds of Data Can Be Mined? -- _t1.4 What Kinds of Patterns Can Be Mined? -- _t1.5 Which Technologies Are Used? -- _t1.6 Which Kinds of Applications Are Targeted? -- _t1.7 Major Issues in Data Mining-- _t1.8 Summary-- _t1.9 Exercises-- _t1.10 Bibliographic Notes-- _tChapter 2. Getting to Know Your Data-- _t2.1 Data Objects and Attribute Types-- _t2.2 Basic Statistical Descriptions of Data-- _t2.-- _t3 Data Visualization-- _t2.4 Measuring Data Similarity and Dissimilarity-- _t2.5 Summary-- _t2.6 Exercises-- _t2.7 Bibliographic Notes-- _tChapter -- _t3. Data Preprocessing-- _t-- _t3.1 Data Preprocessing: An Overview-- _t3.2 Data Cleaning-- _t3.3 Data Integration-- _t3.4 Data Reduction-- _t3.5 Data Transformation and Data iscretization-- _t3.6 Summary-- _t3.7 Exercises-- _t3.8 Bibliographic Notes-- _tChapter -- _t4. Data Warehousing and Online Analytical Processing-- _t4.1 Data Warehouse: Basic Concepts-- _t4.2 Data Warehouse Modeling: Data Cube and OLAP-- _t4. 3 Data Warehouse Design and Usage-- _t4.4 Data Warehouse Implementation-- _t4.5 Data Generalization by Attribute-Oriented Induction-- _t4.6 Summary-- _t4.7 Exercises-- _t4.8 Bibliographic Notes-- _tChapter 5. Data Cube Technology-- _t5.1 Data Cube Computation: Preliminary Concepts-- _t5.2 Data Cube Computation Methods-- _t5. 3 Processing Advanced Kinds of Queries by Exploring Cube Technology-- _t5.4 Multidimensional Data Analysis in Cube Space-- _t5.5 Summary-- _t5.6 Exercises-- _t5.7 Bibliographic Notes-- _tChapter 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods-- _t6.1 Basic Concepts-- _t6.2 Frequent Itemset Mining Methods-- _t6. 3 Which Patterns Are Interesting?--Pattern Evaluation Methods-- _t6.4 Summary-- _t6.5 Exercises-- _t6.6 Bibliographic Notes-- _tChapter t 7. Advanced Pattern Mining-- _t7.1 Pattern Mining: A Road Map-- _t7.2 Pattern Mining in Multilevel, Multidimensional Space-- _t7. 3 Constraint-Based Frequent Pattern Mining-- _t7.4 Mining High-Dimensional Data and Colossal Patterns-- _t7.5 Mining Compressed or Approximate Patterns-- _t7.6 Pattern Exploration and Application-- _t7.7 Summary-- _t7.8 Exercises-- _t7.9 Bibliographic Notes-- _tChapter 8. Classification: Basic Concepts-- _t8.1 Basic Concepts-- _t8.2 Decision Tree Induction-- _t8.3 Bayes Classification Methods-- _t8.4 Rule-Based Classification-- _t8.5 Model Evaluation and Selection-- _t8.6 Techniques to Improve Classification Accuracy-- _t8.7 Summary-- _t8.8 Exercises-- _t8.9 Bibliographic Notes-- _tChapter 9. Classification: Advanced Methods-- _t9.1 Bayesian Belief Networks-- _t9.2 Classification by Backpropagation-- _t9.3 Support Vector Machines-- _t9.4 Classification Using Frequent Patterns-- _t9.5 Lazy Learners (or Learning from Your Neighbors) -- _t9.6 Other Classification Methods-- _t9.7 Additional Topics Regarding Classification-- _t9.8 Summary-- _t9.9 Exercises-- _t9.10 Bibliographic Notes-- _tChapter 10. Cluster Analysis: Basic Concepts and Methods-- _t10.1 Cluster Analysis-- _t10.2 Partitioning Methods-- _t10.t3 Hierarchical Methods-- _t10.4 Density-Based Methods-- _t10.5 Grid-Based Methods-- _t10.6 Evaluation of Clustering-- _t10.7 Summary-- _t10.8 Exercises-- _t10.9 Bibliographic Notes-- _tChapter 11. Advanced Cluster Analysis-- _t11.1 Probabilistic Model-Based Clustering-- _t11.2 Clustering High-Dimensional Data-- _t11.-- _t3 Clustering Graph and Network Data-- _t11.4 Clustering with Constraints-- _t11.5 Summary-- _t11.6 Exercises-- _t11.7 Bibliographic Notes-- _tChapter 12. Outlier Detection-- _t12.1 Outliers and Outlier Analysis-- _t12.2 Outlier Detection Methods-- _t12. 3 Statistical Approaches-- _t12.4 Proximity-Based Approaches-- _t12.5 Clustering-Based Approaches-- _t12.6 Classification-Based Approaches-- _t12.7 Mining Contextual and Collective Outliers-- _t12.8 Outlier Detection in High-Dimensional Data-- _t12.9 Summary-- _t12.10 Exercises12.11 Bibliographic Notes-- _tChapter 13. Data Mining Trends and Research Frontiers-- _t13.1 Mining Complex Data Types-- _t13.2 Other Methodologies of Data Mining-- _t1-- _t3.3 Data Mining Applications-- _t13.4 Data Mining and Society-- _t13.5 Data Mining Trends-- _t13.6 Summary-- _t13.7 Exercises-- _t13.8 Bibliographic Notes-- _tBibliography-- _tIndex.
650		0	_aData mining _917634
690		0	_a0023 วิศวกรรมศาสตรบัณฑิต สาขาวิศวกรรมคอมพิวเตอร์ CPE (ป.ตรี) _965
690		0	_a0021 วิทยาศาสตร์บัณฑิต สาขาเทคโนโลยีสารสนเทศ IT (ป.ตรี) _964
700	1		_aKamber, Micheline _938925
942			_cBK
988			_c29808
999			_c29808 _d29808