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- ISBN 10 7111391403
- ISBN 13 9787111391401
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- Languechinois
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Huazhang Education Computer Science Series Data Mining: Concepts and Techniques (3rd edition of the original book)(Chinese Edition)
Vendeur : liu xing, Nanjing, JS, Chine
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paperback. Etat : New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback. Pub Date: 2012 Pages: 468 Publisher: the Machine Press SYkc basic information about the title: Huazhang Education Computer Science Series Data Mining: Concepts and Techniques (3) of the original book Price: 79.00 yuan Author: Han Wei Press: mechanical Industry Publishing Date: August 14. 2012 ISBN: 9787111391401 words: Page: 468 Edition: 3 Binding: Paperback: Weight: 739 g Editor's Choice Data Mining: Concepts and Techniques (original 3 Edition) Editor's Choice: data mining milestone most classics the full comprehensive exposition of important knowledge and technological innovation in the field. Data Mining: Concepts and Techniques (3) of the original book. Data Mining and Knowledge Discovery must-read reference book for all teachers in the field. researchers. developers and users. is a suitable for data analysis. data Mining and Knowledge Discovery course excellent teaching material. can be used as data mining introductory textbooks of senior undergraduate or first-year graduate students. Summary Data Mining: Concepts and Techniques (3) of the original book. a complete and comprehensive about the concept of data mining methods. techniques and the latest research progress. Data Mining: Concepts and Techniques (original version 3) a comprehensive revision of the previous two editions. strengthen and reorganize the technical content of the book focuses on the data preprocessing. frequent pattern mining. classification and clustering the contents. but also a full description of the OLAP and outlier detection. and mining networks. complex data types. as well as an important application field. Directory publisher Translator's Introduction of the Chinese version of the sequence translator sequence 3 Sequence 2 of sequence Preface Acknowledgements Introduction Chapter 1 Introduction 1.1 Why data mining 1.1.1 1.1.2 Data Mining towards the information age is information technology evolutionary 1.2 What is a data mining 1.3 what type of data 1.3.1 database data 1.3.2 data warehouse 1.3.3 transaction data 1.3.4 Other types of data can be excavated 1.4 1.4.1 What type of mode / concept description can be tapped : characterization and distinguish 1.4.2 mining frequent pattern. association and 1.4.3 for predictive analysis classification and regression clustering 1.4.4 Analysis 1.4.5 Outlier Analysis 1.4.6 All modes are interesting the 1.5 to machine learning what technology 1.5.1 statistically 1.5.2 1.5.3 database systems and data warehouses 1.5.4 Information Retrieval 1.6 for what types of applications 1.6.1 Business Intelligence the 1.6.2 Web search engine 1.7 data mining question 1.7.1 Mining Methods 1.7.2 User Interface 1.7.3 effectiveness and scalability 1.7.4 database type diversity 1.7.5 Data Mining and Social 1.8 Summary 1.9 Exercises 1.10 Bibliographic Notes Chapter 2 understanding of data 2.1 Data Objects Attribute Type 2.1.1 What is the attribute 2.1.2 nominal attributes 2.1.3 two yuan properties 2.1.4 ordinal attributes 2.1.5 value attribute 2.1.6 discrete attributes and continuous attributes the basic statistics of the data 2.2 Description 2.2.1 center trend metrics: the mean. median and mode number 2.2.2 metric data spread: very poor quartile. variance. standard deviation. and quartile poor basic statistical description 2.2.3 Data 2.3 data graphic display can visualization 2.3.1 2.3.2 geometric visualization technique based on the pixel the projection visualization techniques 2.3.3 2.3.4 level visualization technology 2.3.5 icon of visualization technology-based visualization of complex objects and relationships 2.4-metric data of similarity and dissimilarity the 2.4.1 data matrix dissimilarity matrix 2.4.2 Nominal proximity of the property adjacent to measure 2.4.3 Binary Feature dissimilarity measure 2.4.4 numerical properties: Minkowski distance 2.4.5 ordinal properties adjacent dissimilarity measure 2.4.6 mix. N° de réf. du vendeur EH032105
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