Transformative Pattern Learning
Final rept. Sep 2001-Nov 2005
MASSACHUSETTS UNIV AMHERST
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Statistical analysis of relational data is a fundamental and novel problem in machine learning and data mining. Such analysis constructs useful statistical models from data about complex relationships among people, places, things, and events. Supported by this research contract, we uncovered fundamental challenges of statistical learning and inference in relational data, we designed and implemented new languages for expressing deterministic and probabilistic dependencies in such data, we developed new algorithms for learning probabilistic models, we implemented an open-source system for knowledge discovery in relational data containing over 40,000 lines of code that has been downloaded more than 1000 times, and we evaluated the utility of those algorithms by undertaking large and realistic applications.