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Proceedings of the Third SIAM International Conference on Data Mining

Proceedings of the Third SIAM International Conference on Data Mining
Author: Daniel Barbara
Publisher: SIAM
Total Pages: 368
Release: 2003-01-01
Genre: Mathematics
ISBN: 9780898715453

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The third SIAM International Conference on Data Mining provided an open forum for the presentation, discussion and development of innovative algorithms, software and theories for data mining applications and data intensive computation. This volume includes 21 research papers.


Proceedings of the Fourth SIAM International Conference on Data Mining

Proceedings of the Fourth SIAM International Conference on Data Mining
Author: Michael W. Berry
Publisher: SIAM
Total Pages: 556
Release: 2004-01-01
Genre: Mathematics
ISBN: 9780898715682

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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 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 data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers.


Proceedings of the Sixth SIAM International Conference on Data Mining

Proceedings of the Sixth SIAM International Conference on Data Mining
Author: Joydeep Ghosh
Publisher: SIAM
Total Pages: 662
Release: 2006-04-01
Genre: Computers
ISBN: 9780898716115

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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 datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high-performance, and principled analysis techniques and algorithms, based on sound statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them.


Proceedings of the Seventh SIAM International Conference on Data Mining

Proceedings of the Seventh SIAM International Conference on Data Mining
Author: Chid Apte
Publisher: Proceedings in Applied Mathema
Total Pages: 674
Release: 2007
Genre: Computers
ISBN:

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The Seventh SIAM International Conference on Data Mining (SDM 2007) continues a series of conferences whose focus is the theory and application of data mining to complex datasets in science, engineering, biomedicine, and the social sciences. These datasets challenge our abilities to analyze them because they are large and often noisy. Sophisticated, highperformance, and principled analysis techniques and algorithms, based on sound statistical foundations, are required. Visualization is often critically important; tuning for performance is a significant challenge; and the appropriate levels of abstraction to allow end-users to exploit sophisticated techniques and understand clearly both the constraints and interpretation of results are still something of an open question.


Proceedings of the Fifth SIAM International Conference on Data Mining

Proceedings of the Fifth SIAM International Conference on Data Mining
Author: Hillol Kargupta
Publisher: SIAM
Total Pages: 670
Release: 2005-04-01
Genre: Mathematics
ISBN: 9780898715934

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The Fifth 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. Advances in information technology and data collection methods have led to the availability of large data sets in commercial enterprises and in a wide variety of scientific and engineering disciplines. The field of data mining draws upon extensive work in areas such as statistics, machine learning, pattern recognition, databases, and high performance computing to discover interesting and previously unknown information in data. This conference results in data mining, including applications, algorithms, software, and systems.


Data Mining: Foundations and Practice

Data Mining: Foundations and Practice
Author: Tsau Young Lin
Publisher: Springer
Total Pages: 562
Release: 2008-08-17
Genre: Technology & Engineering
ISBN: 3540784888

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The IEEE ICDM 2004 workshop on the Foundation of Data Mining and the IEEE ICDM 2005 workshop on the Foundation of Semantic Oriented Data and Web Mining focused on topics ranging from the foundations of data mining to new data mining paradigms. The workshops brought together both data mining researchers and practitioners to discuss these two topics while seeking solutions to long standing data mining problems and stimul- ing new data mining research directions. We feel that the papers presented at these workshops may encourage the study of data mining as a scienti?c ?eld and spark new communications and collaborations between researchers and practitioners. Toexpressthevisionsforgedintheworkshopstoawiderangeofdatam- ing researchers and practitioners and foster active participation in the study of foundations of data mining, we edited this volume by involving extended and updated versions of selected papers presented at those workshops as well as some other relevant contributions. The content of this book includes st- ies of foundations of data mining from theoretical, practical, algorithmical, and managerial perspectives. The following is a brief summary of the papers contained in this book.