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Relational Data Pre-Processing Techniques for Improved Securities Fraud Detection

Relational Data Pre-Processing Techniques for Improved Securities Fraud Detection

This video was recorded at 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Jose 2007. Commercial datasets are often large, relational, and dynamic. They contain many records of people, places, things, events and their interactions over time. Such datasets are rarely structured appropriately for knowledge discovery, and they often contain variables whose meanings change across different subsets of the data. We describe how these challenges were addressed in a collaborative analysis project undertaken by the University of Massachusetts Amherst and the National Association of Securities Dealers (NASD). We describe several methods for data preprocessing that we applied to transform a large, dynamic, and relational dataset describing nearly the entirety... Show More
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