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Principles of data mining

Principles of data mining (Loan 1 times)

Material type
단행본
Personal Author
Bramer, M. A. (Max A.) , 1948-.
Title Statement
Principles of data mining / Max Bramer.
Publication, Distribution, etc
London :   Springer ,   c2007.  
Physical Medium
x, 343 p. : ill. ; 24 cm.
Series Statement
Undergraduate topics in computer science , 1863-7310
ISBN
9781846287657 (pbk.) 1846287650 (pbk.) :
Bibliography, Etc. Note
Includes bibliographical references (p. [255]-256) and index.
Subject Added Entry-Topical Term
Data mining.
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008 070205s2007 enka b 001 0 eng d
010 ▼a 2007922358
015 ▼a GBA6A6626 ▼2 bnb
020 ▼a 9781846287657 (pbk.)
020 ▼a 1846287650 (pbk.) : ▼c EUR29.95
035 ▼a (KERIS)BIB000010959353
040 ▼a UKM ▼c UKM ▼d BAKER ▼d C#P ▼d DLC ▼d 241002 ▼d 244002
042 ▼a lccopycat
050 0 0 ▼a QA76.9.D343 ▼b B73 2007
082 0 4 ▼a 006.312 ▼2 22
090 ▼a 006.312 ▼b B815p
100 1 ▼a Bramer, M. A. ▼q (Max A.) , ▼d 1948-.
245 1 0 ▼a Principles of data mining / ▼c Max Bramer.
260 ▼a London : ▼b Springer , ▼c c2007.
300 ▼a x, 343 p. : ▼b ill. ; ▼c 24 cm.
440 0 ▼a Undergraduate topics in computer science , ▼x 1863-7310
504 ▼a Includes bibliographical references (p. [255]-256) and index.
650 0 ▼a Data mining.

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Sejong Academic Information Center/Science & Technology/ Call Number 006.312 B815p Accession No. 151243968 Availability Available Due Date Make a Reservation Service C

Contents information

Table of Contents

Introduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naive 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.- Association Rule Mining I.- Association Rule Mining II.- Clustering.- Text Mining.- References.- Appendix A: Essential Mathematics.- Appendix B: Datasets.- Appendix C: Sources of Further Information.- Appendix D: Glossary and Notation.- Appendix E: Solutions to Self-assessment Exercises.- Index.


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