Data mining and mathematical programming
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Bibliographic Information
Data mining and mathematical programming
（CRM proceedings & lecture notes, v. 45）
American Mathematical Society, c2008
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
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Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
Support vector machines and distance minimization by E. Carrizosa 0-1 semidefinite programming for graph-cut clustering: Modelling and approximation by H. Chen and J. Peng Artificial attributes in analyzing biomedical databases by Z. Csizmadia, P. L. Hammer, and B. Vizvari Recent advances in mathematical programming for classification and cluster analysis by Y.-J. Fan, C. Iyigun, and W. A. Chaovalitwongse Nonlinear skeletons of data sets and applications-Methods based on subspace clustering by P. G. Georgiev Current classification algorithms for biomedical applications by M. R. Guarracino, S. Cuciniello, D. Feminiano, G. Toraldo, and P. M. Pardalos Bilevel model selection for support vector machines by M. Kunapuli, K. P. Bennett, J. Hu, and J.-S. Pang Algorithms for detecting complete and partial horizontal gene transfers: Theory and practice by V. Makarenkov, A. Boc, A. Boubacar Diallo, and A. Banire Diallo Nonlinear knowledge in kernel machines by O. L. Mangasarian and E. W. Wild Ultrametric embedding: Application to data fingerprinting and to fast data clustering by F. Murtagh Selective linear and nonlinear classification by O. Seref, O. E. Kundakcioglu, and P.M. Pardalos.
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