Proceedings Of The 2001 Siam International Conference On Data Mining
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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 |
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 Fourth SIAM International Conference on Data Mining
| Author | : Michael W. Berry |
| Publisher | : SIAM |
| Total Pages | : 556 |
| Release | : 2004-01-01 |
| Genre | : Mathematics |
| ISBN | : 9780898715682 |
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 Fifth SIAM International Conference on Data Mining
| Author | : Hillol Kargupta |
| Publisher | : SIAM |
| Total Pages | : 670 |
| Release | : 2005-04-01 |
| Genre | : Mathematics |
| ISBN | : 9780898715934 |
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.
Proceedings 2003 VLDB Conference
| Author | : VLDB |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 1185 |
| Release | : 2003-12-02 |
| Genre | : Computers |
| ISBN | : 0080539785 |
Proceedings of the 29th Annual International Conference on Very Large Data Bases held in Berlin, Germany on September 9-12, 2003. Organized by the VLDB Endowment, VLDB is the premier international conference on database technology.
Data Mining
| Author | : Robert Stahlbock |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 387 |
| Release | : 2009-11-10 |
| Genre | : Computers |
| ISBN | : 1441912800 |
Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including computer science, statistics, operations research, and information systems. Data mining supports a wide range of applications, from medical decision making, bioinformatics, web-usage mining, and text and image recognition to prominent business applications in corporate planning, direct marketing, and credit scoring. Research in information systems equally reflects this inter- and multidisciplinary approach, thereby advocating a series of papers at the intersection of data mining and information systems research. This special issue of Annals of Information Systems contains original papers and substantial extensions of selected papers from the 2007 and 2008 International Conference on Data Mining (DMIN’07 and DMIN’08, Las Vegas, NV) that have been rigorously peer-reviewed. The issue brings together topics on both information systems and data mining, and aims to give the reader a current snapshot of the contemporary research and state of the art practice in data mining.
Encyclopedia of Data Warehousing and Mining, Second Edition
| Author | : Wang, John |
| Publisher | : IGI Global |
| Total Pages | : 2542 |
| Release | : 2008-08-31 |
| Genre | : Computers |
| ISBN | : 1605660116 |
There are more than one billion documents on the Web, with the count continually rising at a pace of over one million new documents per day. As information increases, the motivation and interest in data warehousing and mining research and practice remains high in organizational interest. The Encyclopedia of Data Warehousing and Mining, Second Edition, offers thorough exposure to the issues of importance in the rapidly changing field of data warehousing and mining. This essential reference source informs decision makers, problem solvers, and data mining specialists in business, academia, government, and other settings with over 300 entries on theories, methodologies, functionalities, and applications.
Multi-aspect Learning
| Author | : Richi Nayak |
| Publisher | : Springer Nature |
| Total Pages | : 191 |
| Release | : 2023-08-28 |
| Genre | : Computers |
| ISBN | : 3031335600 |
This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.
