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LEARNING PYTHON, 5TH EDITION POWERFUL OBJECT-ORIENTED PROGRAMMING
NRS 2560.00
 
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DATA MINING CONCEPTS AND TECHNIQUES
The 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 an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. this is the resource you need if you want to apply today’s most powerful data mining techniques to meet real business challenges. data mining: concepts and techniques, 3rd edition chapter 1. introduction 1 what motivated data mining? why is it important? 2 so, what is data mining? 3 data mining--on what kind of data? 4 data mining functionalities-what kinds of patterns can be mined? 5 are all of the patterns interesting? 6 classification of data mining systems 7 data mining task primitives 8 integration of a data mining system with a database or data warehouse system 9 major issues in data mining 10 summary exercises bibliographic notes chapter 2. getting to know your data 1. types of data sets and attribute values 2. basic statistical descriptions of data 3. data visualization 4. measuring data similarity 5. summary exercises bibliographic notes chapter 3. preprocessing 1. data quality 2. major tasks in data preprocessing 3. data reduction 4. data transformation and data discretization 5. data cleaning and data integration 6. summary exercises bibliographic notes chapter 4. data warehousing and on-line analytical processing 1. data warehouse: basic concepts 2. data warehouse modeling: data cube and olap 3. data warehouse design and usage 4. data warehouse implementation 5. data generalization by attribute-oriented induction 6. summary exercises bibliographic notes chapter 5. data cube technology 1. efficient methods for data cube computation 2. exploration and discovery in multidimensional databases 3.. summary exercises bibliographic notes chapter 6. mining frequent patterns, associations and correlations: concepts and methods 1. basic concepts 2. e±cient and scalable frequent itemset mining methods 3. are all the pattern interesting?|pattern evaluation methods 4. applications of frequent pattern and associations 5. summary exercises chapter 7. advanced frequent pattern mining 1. frequent pattern and association mining: a road map 2. mining various kinds of association rules 3. constraint-based frequent pattern mining 4. extended applications of frequent patterns 5. summary exercises bibliographic notes chapter 8. classification: basic concepts 1. classification: basic concepts 2. decision tree induction 3. bayes classi¯cation methods 4. rule-based classi¯cation 5. model evaluation and selection 6. techniques to improve classi¯cation accuracy: ensemble methods 7. handling di®erent kinds of cases in classi¯cation 8. summary exercises bibliographic notes chapter 9. classification: advanced methods 1. bayesian belief networks 2. classi¯cation by neural networks 3. support vector machines 4. pattern-based classi¯cation 5. lazy learners (or learning from your neighbors) 6. other classi¯cation methods 7. summary exercises bibliographic notes chapter 10. cluster analysis: basic concepts and methods 1. cluster analysis: basic concepts 2. clustering structures 3. major clustering approaches 4. partitioning methods 5. hierarchical methods 6. density-based methods 7. model-based clustering: the expectation-maximization method 8. other clustering techniques 9. summary exercises bibliographic notes chapter 11. advanced cluster analysis 1. clustering high-dimensional data 2. constraint-based and user-guided cluster analysis 3. link-based cluster analysis 4. semi-supervised clustering and classi¯cation 5. bi-clustering 6. collaborative ¯ltering 7. summary exercises bibliographic notes chapter 12. outlier analysis 1. why outlier analysis? identifying and handling of outliers 2. distribution-based outlier detection: a statistics-based approach 3. classi¯cation-based outlier detection 4. clustering-based outlier detection 5. deviation-based outlier detection 6. isolation-based method: from isolation tree to isolation forest 7. summary exercises bibliographic notes chapter 13. trends and research frontiers in data mining 1. mining complex types of data 2. advanced data mining applications 3. data mining system products and research prototypes 4. social impacts of data mining 5. trends in data mining 6. summary exercises bibliographic notes appendix a: an introduction to microsoft's ole db for data mining

Author : Han,kamber,pei
Publication : Elsevier
Isbn : 9789380931913
Store book number : 106
NRS 1000.00
  
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