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| 008 | 050303s2005 ne a b 001 0 eng | |
| 010 | ▼a 2005043385 | |
| 020 | ▼a 0120884070 | |
| 020 | ▼a 9780120884070 | |
| 040 | ▼a DLC ▼c DLC ▼d DLC ▼d 211009 | |
| 042 | ▼a pcc | |
| 050 | 0 0 | ▼a QA76.9.D343 ▼b W58 2005 |
| 082 | 0 4 | ▼a 006.312 ▼2 22 |
| 090 | ▼a 006.312 ▼b W829d2 | |
| 100 | 1 | ▼a Witten, I. H. ▼q (Ian H.), ▼d 1947- ▼0 AUTH(211009)142645. |
| 245 | 1 0 | ▼a Data mining : ▼b practical machine learning tools and techniques / ▼c Ian H. Witten, Eibe Frank. |
| 250 | ▼a 2nd ed. | |
| 260 | ▼a Amsterdam ; ▼a Boston, MA : ▼b Morgan Kaufman, ▼c 2005. | |
| 300 | ▼a xxxi, 525 p. : ▼b ill. ; ▼c 24 cm. | |
| 440 | 0 | ▼a Morgan Kaufmann series in data management systems |
| 504 | ▼a Includes bibliographical references (p. 485-503) and index. | |
| 650 | 0 | ▼a Data mining. |
| 700 | 1 | ▼a Frank, Eibe ▼0 AUTH(211009)33455. |
| 945 | ▼a KINS |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 006.312 W829d2 | 등록번호 111468074 (9회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. 2 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.312 W829d2 | 등록번호 121115995 (28회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. 3 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.312 W829d2 | 등록번호 121190770 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. 4 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.312 W829d2 | 등록번호 121190771 (4회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. 5 | 소장처 세종학술정보원/과학기술실(5층)/ | 청구기호 006.312 W829d2 | 등록번호 151232964 (2회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 006.312 W829d2 | 등록번호 111468074 (9회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.312 W829d2 | 등록번호 121115995 (28회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. 2 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.312 W829d2 | 등록번호 121190770 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. 3 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.312 W829d2 | 등록번호 121190771 (4회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 세종학술정보원/과학기술실(5층)/ | 청구기호 006.312 W829d2 | 등록번호 151232964 (2회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references.
The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more.
This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.
Reviews
“I was a big fan of the first edition and I'm excited about this new edition.?? Peter Norvig, Director of Search Quality, Google, Inc.
“This book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples, and urging the reader to consider the simple techniques first, and then progress to the more sophisticated ones if the simple ones prove inadequate. If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start.?
? From the foreword by Jim Gray, Microsoft Research
“It covers cutting-edge, data mining technology that forward-looking organizations use to successfully tackle problems that are complex, highly dimensional, chaotic, non-stationary (changing over time), or plagued by. The writing style is well-rounded and engaging without subjectivity, hyperbole, or ambiguity. I consider this book a classic already!?
? Dr. Tilmann Bruckhaus, StickyMinds.com
Feature
- Algorithmic methods at the heart of successful data mining?including tried and true techniques as well as leading edge methods
- Performance improvement techniques that work by transforming the input or output
정보제공 :
목차
Preface
1. What’s it all about?
2. Input: Concepts, instances, attributes
3. Output: Knowledge representation
4. Algorithms: The basic methods
5. Credibility: Evaluating what’s been learned
6. Implementations: Real machine learning schemes
7. Transformations: Engineering the input and output
8. Moving on: Extensions and applications
Part II: The Weka machine learning workbench
9. Introduction to Weka
10. The Explorer
11. The Knowledge Flow interface
12. The Experimenter
13. The command-line interface
14. Embedded machine learning
15. Writing new learning schemes
References
Index
정보제공 :