LEADER 05507nam 2200697 a 450 001 9910461363903321 005 20200520144314.0 010 $a1-283-17117-1 010 $a9786613171177 010 $a0-12-381480-4 035 $a(CKB)2670000000092948 035 $a(EBL)729031 035 $a(OCoLC)741491891 035 $a(SSID)ssj0000507801 035 $a(PQKBManifestationID)12207267 035 $a(PQKBTitleCode)TC0000507801 035 $a(PQKBWorkID)10550628 035 $a(PQKB)10134696 035 $a(MiAaPQ)EBC729031 035 $a(CaSebORM)9780123814791 035 $a(PPN)170267180 035 $a(Au-PeEL)EBL729031 035 $a(CaPaEBR)ebr10483440 035 $a(CaONFJC)MIL317117 035 $a(EXLCZ)992670000000092948 100 $a20110405d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aData mining$b[electronic resource] $econcepts and techniques /$fJiawei Han, Micheline Kamber, Jian Pei 205 $a3rd ed. 210 $aBurlington, Mass. $cElsevier$dc2012 215 $a1 recurso en li?nea (745 páginas) 225 1 $aThe Morgan Kaufmann series in data management systems 300 $aDescription based upon print version of record. 311 $a0-12-381479-0 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Data Mining: Concepts and Techniques; Copyright; Dedication; Table of Contents; Foreword; Foreword to Second Edition; Preface; Acknowledgments; About the Authors; Chapter 1. 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; Chapter 2. Getting to Know Your Data; 2.1 Data Objects and Attribute Types 327 $a2.2 Basic Statistical Descriptions of Data2.3 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 Discretization; 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 327 $a4.4 Data Warehouse Implementation4.5 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 327 $a6.3 Which Patterns Are Interesting?-Pattern Evaluation Methods6.4 Summary; 6.5 Exercises; 6.6 Bibliographic Notes; Chapter 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 327 $a8.3 Bayes Classification Methods8.4 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 327 $aChapter 10. Cluster Analysis: Basic Concepts and Methods 330 $aThe increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with 410 0$aMorgan Kaufmann series in data management systems. 606 $aData mining 608 $aElectronic books. 615 0$aData mining. 676 $a006.3/12 700 $aHan$b Jiawei$0145005 701 $aKamber$b Micheline$0145006 701 $aPei$b Jian$0868267 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910461363903321 996 $aData mining$92466281 997 $aUNINA