People Innovation Excellence

DATA VISUALIZATION (2 Credits)

Learning Outcomes:

On successful completion of this Course, students will be able to: Describe Typical Documents in Large Collections of Documents, Data Mining Techniques for Outlier Detection, and Ontology-Based Framework; Classify Dimensionality Reduction for Interactive Visual Clustering and Database Analysis with ANNs by means of Graph Evolution; Identify An Optimal Categorization of Feature Selection Methods for Knowledge Discovery and Visual Survey Analysis in Marketing; and Design Assessing Data Mining Approaches for Analyzing Actuarial Student Success Rate, Web Mining and Social Network Analysis.

Topics:

  1. Towards the Notion of Typical Documents in Large Collections of Documents;
  2. Data Mining Techniques for Outlier Detection;
  3. Using an Ontology-Based Framework to Extract External Web Data for the Data Warehouse;
  4. Dimensionality Reduction for Interactive Visual Clustering: A Comparative Analysis;
  5. tabase Analysis with ANNs by means of Graph Evolution;
  6. An Optimal Categorization of Feature Selection Methods for Knowledge Discovery;
  7. From Data to Knowledge: Data Mining;
  8. Patent Infringement Risk Analysis Using Rough Set Theory;
  9. isual Survey Analysis in Marketing;
  10. Assessing Data Mining Approaches for Analyzing Actuarial Student Success Rate;
  11. A Robust Biclustering Approach for Effective Web Personalization;
  12. Web Mining and Social Network Analysis;
  13. iVAS: An Interactive Visual Analytic System for Frequent Set Mining;
  14. Mammogram Mining Using Genetic Ant-Miner;
  15. Use of SciDBMaker as Tool for the Design of Specialized Biological Databases;
  16. Interactive Visualization Tool for Analysis of Large Image Databases;
  17. Supercomputers and Supercomputing.

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