Date of Award
3-21-2013
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Advisor
Peiyi Tang
Abstract
items) which do not appear in the database. Given this taxonomy, there exist generalized items which do not appear in the uncertain database, but nevertheless, can be probabilistically frequent within. Thus, a new method is introduced which calculates the probability of a generalized item appearing within a transaction, and thus, one can then mine for PGFIs in an uncertain database.
Recommended Citation
Peterson, Erich Allen, "New Algorithms for Frequent Sequential Pattern and Itemset Data Mining in Certain and Uncertain Databases" (2013). Theses and Dissertations. 403.
https://research.ualr.edu/etd/403
