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The Resource Principles of data mining, Max Bramer

Principles of data mining, Max Bramer

Label
Principles of data mining
Title
Principles of data mining
Statement of responsibility
Max Bramer
Creator
Subject
Language
eng
Summary
"Data mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas." "This book explains and explores the principal techniques of Data Mining for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples & explanations of the algorithms given."--BOOK JACKET
Member of
Cataloging source
UKM
http://library.link/vocab/creatorDate
1948-
http://library.link/vocab/creatorName
Bramer, M. A.
Dewey number
006.312
Illustrations
illustrations
Index
index present
LC call number
QA76.9.D343
LC item number
B73 2007
Literary form
non fiction
Nature of contents
bibliography
Series statement
Undergraduate topics in computer science,
http://library.link/vocab/subjectName
Data mining
Label
Principles of data mining, Max Bramer
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
1. Introduction to data mining -- 2. Data for data mining -- 3. Introduction to Classification: naïve Bayes and nearest neighbour -- 4. Using decision trees for classification -- 5. Decision tree induction: using entropy for attribute selection -- 6. Decision tree induction: using frequency tables for attribute selection -- 7. Estimating the predictive accuracy of a classifier -- 8. Continuous attributes -- 9. Avoiding overfitting of decision trees -- 10. More about entropy -- 11. Inducing modular rules for classification -- 12. Measuring the performance of a classifier -- 13. Dealing with large volumes of data -- 14. Ensemble classification -- 15. Comparing classifiers -- 16. Association rule mining I -- 17. Association rule mining II -- 18. Association rule mining III: frequent pattern trees -- 19. Clustering -- 20. Text mining -- Appendices
Control code
840485704
Dimensions
24 cm
Edition
2nd ed.
Extent
xiv, 440 pages
Isbn
9781447148838
Lccn
2013932775
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
(OCoLC)840485704
Label
Principles of data mining, Max Bramer
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
1. Introduction to data mining -- 2. Data for data mining -- 3. Introduction to Classification: naïve Bayes and nearest neighbour -- 4. Using decision trees for classification -- 5. Decision tree induction: using entropy for attribute selection -- 6. Decision tree induction: using frequency tables for attribute selection -- 7. Estimating the predictive accuracy of a classifier -- 8. Continuous attributes -- 9. Avoiding overfitting of decision trees -- 10. More about entropy -- 11. Inducing modular rules for classification -- 12. Measuring the performance of a classifier -- 13. Dealing with large volumes of data -- 14. Ensemble classification -- 15. Comparing classifiers -- 16. Association rule mining I -- 17. Association rule mining II -- 18. Association rule mining III: frequent pattern trees -- 19. Clustering -- 20. Text mining -- Appendices
Control code
840485704
Dimensions
24 cm
Edition
2nd ed.
Extent
xiv, 440 pages
Isbn
9781447148838
Lccn
2013932775
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
(OCoLC)840485704

Library Locations

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