CRM Proceedings & Lecture Notes
Volume: 45;
2008;
234 pp;
Softcover
MSC: Primary 68; 90;
Print ISBN: 978-0-8218-4352-9
Product Code: CRMP/45
List Price: $93.00
AMS Member Price: $74.40
MAA Member Price: $83.70
Electronic ISBN: 978-1-4704-3959-0
Product Code: CRMP/45.E
List Price: $93.00
AMS Member Price: $74.40
MAA Member Price: $83.70
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Data Mining and Mathematical Programming
Share this pageEdited by Panos M. Pardalos; Pierre Hansen
A co-publication of the AMS and Centre de Recherches Mathématiques
Data mining aims at finding interesting, useful or profitable information in
very large databases. The enormous increase in the size of available
scientific and commercial databases (data avalanche) as well as the
continuing and exponential growth in performance of present day computers
make data mining a very active field. In many cases, the burgeoning volume
of data sets has grown so large that it threatens to overwhelm rather than
enlighten scientists. Therefore, traditional methods are revised and
streamlined, complemented by many new methods to address challenging new
problems. Mathematical Programming plays a key role in this endeavor. It
helps us to formulate precise objectives (e.g., a clustering criterion or a
measure of discrimination) as well as the constraints imposed on the
solution (e.g., find a partition, a covering or a hierarchy in clustering).
It also provides powerful mathematical tools to build highly performing
exact or approximate algorithms.
This book is based on lectures presented at the workshop on "Data Mining and
Mathematical Programming" (October 10–13, 2006, Montreal) and will be a
valuable scientific source of information to faculty, students, and
researchers in optimization, data analysis and data mining, as well as
people working in computer science, engineering and applied
mathematics.
Titles in this series are co-published with the Centre de Recherches Mathématiques.
Readership
Graduate students and research mathematicians interested in optimization, data analysis, and data mining.
Table of Contents
Data Mining and Mathematical Programming
- Cover Cover11
- Title page iii4
- Contents v6
- Preface vii8
- Support vector machines and distance minimization 110
- 0-1 semidefinite programming for graph-cut clustering: Modelling and approximation 1524
- Artificial attributes in analyzing biomedical databases 4150
- Recent advances in mathematical programming for classification and cluster analysis 6776
- Nonlinear skeletons of data sets and applications—Methods based on subspace clustering 95104
- Current classification algorithms for biomedical applications 109118
- Bilevel model selection for support vector machines 129138
- Algorithms for detecting complete and partial horizontal gene transfers: Theory and practice 159168
- Nonlinear knowledge in kernel machines 181190
- Ultrametric embedding: Application to data fingerprinting and to fast data clustering 199208
- Selective linear and nonlinear classification 211220
- Back Cover Back Cover1246