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Separation Model for Data Mining Optimization Workshop

Explore the innovative Separation Model for data mining optimization, applications in credit risk aggregation and customer segmentation, future research prospects including convergence analysis, and integration with existing tools.

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Separation Model for Data Mining Optimization Workshop

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  1. GIEG Workshop 2007 Erick Moreno Centeno Industrial Engineer and Operations Reserach Univ. of California, Berkeley

  2. Current Research • The use of a new optimization technique for data mining and pattern recognition. • Separation model (proposed by Prof. Hochbaum) • May be used as an integration tool, once the data has been processed by other data mining tools. • Explicitly is designed to be able to include subjective information and exploit the pairwise relationships between the observations.

  3. The Separation Model

  4. Robustness of the Separation Model • Reviewer #3 reverses the implied ranking of the first two reviewers when computing the aggregate ranking using the average rule. • The separation model’s output fully agrees with the first two reviewers. *when fixing the node potential of project #1 to 0

  5. Robustness of the Separation Model (2) • Perhaps reviewer #3 can reverse the implied ranking of the first two reviewers by increasing exponentially the separation between the projects. • The separation model’s output fully agrees with the first two reviewers. *when fixing the node potential of project #1 to 0

  6. Current Research • Applications • Country credit risk aggregation • Customer segmentation

  7. Future Research • Analysis of the new techniques their properties and implementations. • Large scale application of the optimization algorithm. • Convergence analysis of a proposed an iterative procedure. • Implementation of a network flow procedure to solve the optimization problem. • Design and analysis of new optimization algorithms

  8. Future Research • Applications • Company clustering via stock correlations

  9. Learn from this Workshop • How to take advantage of the cyber infrastructure tools for the proposed algorithm? • How can this model be implemented in the SDCS? • How does the proposed data mining model compares to other tools available in the SDSC? • How can this tool help integrate the tools available?

  10. Learn from this Workshop • Develop skills and experience to incorporate cyberinfrastructure tools in future research projects. Specifically the design of optimization algorithms to take explicit advantage of this tools.

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