04756nam a2200193 a 45000010006000000050017000060080041000230200018000640500027000821000016001092450093001252500012002182600040002303000026002705040019002965054209003156500016045247000022045402980820230531230839.0130910s2012    us m         a001 0 eng d  a9780123814791 4aQA76.9.D343bH233 20121 aHan, Jiawei10aData mining :bconcepts and techniques /cJiawei Han, Micheline Kamber, Jian Peih[book]  a3rd ed.  aBurlington, MA :bElsevier,cc2012.  axxxii, 703 p. :bill.  aincludes index 0aChapter 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. 0aData mining1 aKamber, Micheline