Ditemukan 256 dokumen yang sesuai dengan query
Groth, Robert
Upper Saddle River: Prentice-Hall, 1998
658.002 8 GRO d (1)
Buku Teks Universitas Indonesia Library
Olson, David Louis
Boston: McGraw-Hill , 2007
650.1 OLS i
Buku Teks Universitas Indonesia Library
Nettleton, David, 1963-
"
Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to ...
"
Amsterdam: Amsterdam Elsevier, 2014
658.056 312 NET c
Buku Teks Universitas Indonesia Library
Aggarwal, Charu C., editor
"
This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on text embedded with heterogeneous and multimedia data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases.
...
"
New York: Springer, 2012
e20407655
eBooks Universitas Indonesia Library
Nettleton, David
"
Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial data mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to ...
"
Waltham, MA: Morgan Kaufmann, 2014
e20426889
eBooks Universitas Indonesia Library
"
The Sixth SIAM International Conference on Data Mining continues the tradition of presenting approaches, tools, and systems for data mining in fields such as science, engineering, industrial processes, healthcare, and medicine. The conference was sponsored by the Center for Applied Scientific Computing at the Lawrence Livermore National Laboratory and the American Statistical Association, continuing a trend towards greater collaboration between the two communities ...
"
Philadelphia: Society for Industrial and Applied Mathematics, 2006
e20449186
eBooks Universitas Indonesia Library
Pearson, Ronald K., 1952-
"
Data mining is concerned with the analysis of databases large enough that various anomalies, including outliers, incomplete data records, and more subtle phenomena such as misalignment errors, are virtually certain to be present. Mining Imperfect Data describes in detail a number of these problems, as well as their sources, their consequences, their detection, and their treatment. Specific strategies for data pretreatment and analytical validation that are broadly applicable are described, making them useful in conjunction ...
"
Philadelphia : Society for Industrial and Applied Mathematics, 2005
e20443143
eBooks Universitas Indonesia Library
"
The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who will discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked ...
"
Philadelphia: Society for Industrial and Applied Mathematics, 2004
e20443198
eBooks Universitas Indonesia Library
Hancock, Monte F., Jr.
Boca Raton: CRC Press, 2012
006.312 HAN p
Buku Teks SO Universitas Indonesia Library
Witten, I.H. (Ian H.)
"
Part I. Machine Learning Tools and Techniques: 1. What?s iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what?s been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge ...
"
Amsterdam: Elsevier , 2011
006.312 WIT d
Buku Teks SO Universitas Indonesia Library
