| Title | A Metapattern-Based Automated Discovery Loop for Integrated Data Mining |
| Type | Award |
| NSF Org | IRI |
| Latest Amendment | |
| Date | May 7, 1996 |
| File | a9529615 |
| Award Number | 9529615 |
| Award Instr | Continuing Grant |
| Prgm Manager | Maria Zemankova |
| Start Date | June 1, 1996 |
| Expires Expected | May 31, 1999 (Estimated) |
| Total Amt. | : $279,253 (Estimated) |
| Investigator | Wei-Min Shen shen@isi.edu |
| Sponsor | U of Southern California,University Park,Los Angeles, CA 900891147 213/740-2934 |
| NSF Program | 6855 DATABASE & EXPERT SYSTEMS |
| Fld Applictn | 0104000 Information Systems |
| Abstract : | This research is developing a metapattern-based discovery loop for integrated data mining. Metapatterns (also known as metaqueries) are second-order, declarative expressions that specify the types of patterns to be discovered and assist humans in focusing on more fruitful search directions. The discovery loop is a search engine that integrates deduction, induction, and external guidance from humans, as well as internal guidance of inter-component dependencies. Given a completely newdatabase, the system first generates an initial set of the most general metapatterns based on the meta-information of the database, and then executes these metapatterns against the database to discover actual patterns. Based on the results, new metapatterns are dynamically generated by adding more constraints to the more plausible metapatterns. In this iterative process, human discovers can analyze, create, select, and execute metapatterns, or instruct the system to pursue metapatterns on its own. This ability not only makes the process of data mining moreefficient and productive (the more expert users can use the system for inspiration of better metapatterns, and the less expert users can learn how to perform data mining in a particular domain by observation), but also provides a new method for unsupervised learning of probabilistic,relation-based patterns. |
