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Data mining and analysis : fundamental concepts and algorithms

Data mining and analysis : fundamental concepts and algorithms (Loan 3 times)

Material type
단행본
Personal Author
Zaki, Mohammed J., 1971-. Meira, Wagner, 1967-.
Title Statement
Data mining and analysis : fundamental concepts and algorithms / Mohammed J. Zaki, Rensselaer Polytechnic Institute, Troy, New York, Wagner Meira, Jr., Universidade Federal de Minas Gerais, Brazil.
Publication, Distribution, etc
New York, NY :   Cambridge University Press,   2014.  
Physical Medium
xi, 593 p. : ill. ; 27 cm.
ISBN
9780521766333 (hardback : alk. paper)
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Data mining.
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001 000045864982
005 20160314175621
008 160314s2014 nyua b 001 0 eng d
010 ▼a 2013037544
020 ▼a 9780521766333 (hardback : alk. paper)
035 ▼a (KERIS)REF000017260708
040 ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009
050 0 0 ▼a QA76.9.D343 ▼b Z36 2014
082 0 0 ▼a 006.3/12 ▼2 23
084 ▼a 006.312 ▼2 DDCK
090 ▼a 006.312 ▼b Z21d
100 1 ▼a Zaki, Mohammed J., ▼d 1971-.
245 1 0 ▼a Data mining and analysis : ▼b fundamental concepts and algorithms / ▼c Mohammed J. Zaki, Rensselaer Polytechnic Institute, Troy, New York, Wagner Meira, Jr., Universidade Federal de Minas Gerais, Brazil.
260 ▼a New York, NY : ▼b Cambridge University Press, ▼c 2014.
300 ▼a xi, 593 p. : ▼b ill. ; ▼c 27 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Data mining.
700 1 ▼a Meira, Wagner, ▼d 1967-.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.312 Z21d Accession No. 121236003 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

1. Data mining and analysis; Part I. Data Analysis Foundations: 2. Numeric attributes; 3. Categorical attributes; 4. Graph data; 5. Kernel methods; 6. High-dimensional data; 7. Dimensionality reduction; Part II. Frequent Pattern Mining: 8. Itemset mining; 9. Summarizing itemsets; 10. Sequence mining; 11. Graph pattern mining; 12. Pattern and rule assessment; Part III. Clustering: 13. Representative-based clustering; 14. Hierarchical clustering; 15. Density-based clustering; 16. Spectral and graph clustering; 17. Clustering validation; Part IV. Classification: 18. Probabilistic classification; 19. Decision tree classifier; 20. Linear discriminant analysis; 21. Support vector machines; 22. Classification assessment.


Information Provided By: : Aladin

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