Optimization Laboratory for Data Mining (OLDAM)

The term Data Mining is used to address a number of methods and techniques that extract relevant information and relations from large data sets. The interest in Data Mining has largely increased in the last years and is motivated by the need of understanding complex phenomena that are "hidden" in large bodies of data and observations.

Data Mining is a set of approaches, models and techniques that may differ in the type of problems that they solve and in their efficacy in solving them.

Some important Data Mining fields, such as Machine Learning, Pattern Recognition, Clustering, present several problems that can be formulted as mathematical programs, where the contribution of the Optimization Research community can be relevant both from a theoretical and practical point of view.

This has motivated the creation a scientific environment devoted to the investigation and the application of optimization techniques for Data Mining problems: OLDAM, the Optimization Laboratory for Data Mining, located in the IASI, the Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti" of the Italian National Research Council (CNR). This laboratory has been born in the beginning of 2002, and finds its roots in several research directions and research projects related to neural networks, support vector machines, learning on logic domains, medical and industrial applications, web mining, to which were devoted the efforts of several researchers and visitors of IASI. The mission of OLDAM may be synthesized in two main objectives:

  • Develop successful applications of Optimization Techniques to Data Mining problems coming from different fields;
  • Integrate the different techniques available, plus the ones newly developed, into a general theoretical framework.

The main activities of OLDAM are:

  • production of scientific results;
  • participation to research projects;
  • collaboration with private companies for Data Mining applications;
  • organization of seminars and conferences;
  • support for graduate and post-graduate dissertations.
Lab's home page
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