Recent Advances in Optimization Models for Data Mining: Clustering, Feature Selection and Classification

Date

2008-09-01

Author

Fan, Y-j
İyigün, Cem
Chaovalitwongse , W. A.

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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.

URI

https://bookstore.ams.org/crmp-45
https://hdl.handle.net/11511/72848

Relation

Data Mining and Mathematical Programming (CRM Proceedings & Lecture Notes, Book 45)

Collections

Department of Industrial Engineering, Book / Book chapter

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Citation Formats

Y.-j. Fan, C. İyigün, and W. A. Chaovalitwongse, Recent Advances in Optimization Models for Data Mining: Clustering, Feature Selection and Classification. 2008, p. 95.