Principles of data mining /

详细信息    2h0038804&isn=978-1-4471-4883-8(pbk.) :">Principles of data mining /

• 出版日期：c2013.
• 出版者：Springer,
• 页数：xiv, 440 p. :
• 出版地：London :
• 第一责任说明：Max Bramer.
• 尺寸：24cm.
• 分类号：a847.8
• ISBN：978-1-4471-4883-8(pbk.) :

MARC全文

02h0038804 20140716170555.0 121123s2013 enka frb |001|||eng | 978-1-4471-4883-8(pbk.) : CNY569.61 UKMGB eng UKMGB OCLCO ; BTCTA ; YDXCP ; BWX ; CDX ; SYB ; CNNGL QA 006.312 23 a847.8 aTP311.13 v5 Bramer, M. A. (Max A.), 1948- Principles of data mining / Max Bramer. 2nd ed. London : Springer, c2013. xiv, 440 p. : ill. ; 24cm. Undergraduate topics in computer science, 1863-7310 Includes bibliographical references and index. Introduction to Data Mining -- ; Data for Data Mining -- ; Introduction to Classification: Nave Bayes and Nearest Neighbour -- ; Using Decision Trees for Classification -- ; Decision Tree Induction: Using Entropy for Attribute Selection -- ; Decision Tree Induction: Using Frequency Tables for Attribute Selection -- ; Estimating the Predictive Accuracy of a Classifier -- ; Continuous Attributes -- ; Avoiding Overfitting of Decision Trees -- ; More About Entropy -- ; Inducing Modular Rules for Classification -- ; Measuring the Performance of a Classifier -- ; Dealing with Large Volumes of Data -- ; Ensemble Classification -- ; Comparing Classifiers -- ; Association Rule Mining I -- ; Association Rule Mining II -- ; Association Rule Mining III: Frequent Pattern Trees -- ; Clustering -- ; Text Mining. Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas.Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail.This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data.Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field.Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. Data mining. Undergraduate topics in computer science. aCN b010001 010001 EB00073739 847.8 B73/2 lxl1306 rCNY569.61