Data Mining and Mathematical ProgrammingPanos M. Pardalos, Pierre Hansen American Mathematical Soc., 2008. gada 9. apr. - 234 lappuses 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. |
Saturs
| 1 | |
| 15 | |
Artificial Attributes in Analyzing Biomedical Databases | 41 |
Recent Advances in Mathematical Programming for Classification and Cluster Analysis | 67 |
Nonlinear Skeletons of Data Sets and ApplicationsMethods Based on Subspace Clustering | 95 |
Current Classification Algorithms for Biomedical Applications | 109 |
Bilevel Model Selection for Support Vector Machines | 129 |
Theory and Practice | 159 |
Nonlinear Knowledge in Kernel Machines | 181 |
Application to Data Fingerprinting and to Fast Data Clustering | 199 |
Selective Linear and Nonlinear Classification | 211 |
Citi izdevumi - Skatīt visu
Data Mining and Mathematical Programming Panos M. Pardalos,Pierre Hansen Priekšskatījums nav pieejams - 2008 |
Bieži izmantoti vārdi un frāzes
0-1 SDP model analysis applied approach approximation artificial attributes bilevel bilevel program binary classification accuracy classification problem clustering algorithm coefficient matrix complementarity constraints computational convex corresponding cross validation data mining data points data set Database defined denote dimensionality distance E-mail address eigenvalues eigenvectors feature selection FIGURE formulation free slack gene tree hierarchical horizontal gene transfer hyper-parameters hyperplane inner-level problems iterative elimination K-means clustering kernel kernel trick labels linear programming loss function machine learning mathematical programming maximize minimization misclassification MPECs negative node nonlinear O. L. Mangasarian objective function obtained optimal solution optimization problem outer-level pairs parameter partition pattern vectors performance phylogenies positive prior knowledge proposed regression SDPCut Section semidefinite skeleton solving space species tree standard SVM subset subtree supervised learning support vector machines Theorem training set ultrametricity variables weighted K-means
Populāri fragmenti
92. lappuse - S. Guha. R. Rastogi, and K. Shim. CURE: An efficient clustering algorithm for large databases.
38. lappuse - J. Shi and J. Malik. Normalized cuts and image segmentation.
38. lappuse - Some methods for classification and analysis of multivariate observations. 5th Berkeley symposium on mathematics, statistics, and probability.
93. lappuse - W. Wang, J. Yang, and RR Muntz, "STING: A statistical information grid approach to spatial data mining," in Proceedings of the 23rd VLDB Conference, pp.
156. lappuse - Chen and OL Mangasarian. A class of smoothing functions for nonlinear and mixed complementarity problems.
67. lappuse - The goal of data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner [18].
38. lappuse - AK Jain and RC Dubes, Algorithms for Clustering Data, Englewood Cliffs, NJ: Prentice Hall, 1988.
197. lappuse - A. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, eds., MIT Press, (Cambridge, MA), 2000. 4. T. Hastie and R. Tibshirani, "Classification by pairwise coupling," The Annals of Statistics 26(1), pp.
178. lappuse - UNIVERSITE DU QUEBEC A MONTREAL, CP 8888, Succ. CENTRE- VILLE, MONTREAL (QUEBEC), H3C 3P8, CANADA.
125. lappuse - G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning.