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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

자료유형
단행본
개인저자
Zaki, Mohammed J., 1971- author. Meira, Wagner, 1967-, author.
서명 / 저자사항
Data mining and machine learning : fundamental concepts and algorithms / Mohammed J. Zaki, Wagner Meira, Jr.
판사항
2nd ed.
발행사항
Cambridge, United Kingdom ; New York, NY :   Cambridge University Press,   2020.  
형태사항
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"--
일반주기
Revised edition of: Data mining and analysis. 2014.  
Includes index.  
일반주제명
Data mining.
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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

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info/지정도서 청구기호 006.312 Z21d2 등록번호 121256771 도서상태 지정도서 반납예정일 예약 서비스 M

컨텐츠정보

목차

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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