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Data mining and machine learning : fundamental concepts and algorithms / 2nd ed

Data mining and machine learning : fundamental concepts and algorithms / 2nd ed

Material type
단행본
Personal Author
Zaki, Mohammed J., 1971- author. Meira, Wagner, 1967-, author.
Title Statement
Data mining and machine learning : fundamental concepts and algorithms / Mohammed J. Zaki, Wagner Meira, Jr.
판사항
2nd ed.
Publication, Distribution, etc
Cambridge, United Kingdom ; New York, NY :   Cambridge University Press,   2020.  
Physical Medium
xii, 766 p. : ill. ; 26 cm.
ISBN
9781108473989 (hardback) 9781108564175 (epub)
요약
"The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts.New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning"--
General Note
Revised edition of: Data mining and analysis. 2014.  
Includes index.  
Subject Added Entry-Topical Term
Data mining.
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010 ▼a 2019037293
020 ▼a 9781108473989 (hardback)
020 ▼a 9781108564175 (epub)
035 ▼a (KERIS)REF000019134955
040 ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009
042 ▼a pcc
050 0 0 ▼a QA76.9.D343 ▼b Z36 2020
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090 ▼a 006.312 ▼b Z21d2
100 1 ▼a Zaki, Mohammed J., ▼d 1971- ▼e author.
240 1 0 ▼a Data mining and analysis
245 1 0 ▼a Data mining and machine learning : ▼b fundamental concepts and algorithms / ▼c Mohammed J. Zaki, Wagner Meira, Jr.
250 ▼a 2nd ed.
260 ▼a Cambridge, United Kingdom ; New York, NY : ▼b Cambridge University Press, ▼c 2020.
300 ▼a xii, 766 p. : ▼b ill. ; ▼c 26 cm.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
500 ▼a Revised edition of: Data mining and analysis. 2014.
500 ▼a Includes index.
520 ▼a "The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts.New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning"-- ▼c Provided by publisher.
650 0 ▼a Data mining.
700 1 ▼a Meira, Wagner, ▼d 1967-, ▼e author.
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 Z21d2 Accession No. 121256771 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; Part V. Regression: 23. Linear regression; 24. Logistic regression; 25. Neural networks; 26. Deep learning; 27. Regression evaluation.

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