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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 |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.312 Z21d | 등록번호 121236003 (3회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.
정보제공 :
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.
정보제공 :