New approaches to weighted frequent pattern mining

New Approaches to Weighted Frequent Pattern Mining. (December 2005) Unil Yun, B.S., Hong Ik University; M.S., Korea University Chair of Advisory Committee: Dr. John J. Leggett Researchers have proposed frequent pattern mining algorithms that are more efficient than previous algorithms and generate fewer but more important patterns. Many techniques such as depth first/breadth first search, use of tree/other data structures, top down/bottom up traversal and vertical/horizontal formats for frequent pattern mining have been developed. Most frequent pattern mining algorithms use a support measure to prune the combinatorial search space. However, support-based pruning is not enough when taking into consideration the characteristics of real datasets. Additionally, after mining datasets to obtain the frequent patterns, there is no way to adjust the number of frequent patterns through user feedback, except for changing the minimum support. Alternative measures for mining frequent patterns have been suggested to address these issues. One of the main limitations of the traditional approach for mining frequent patterns is that all items are treated uniformly when, in reality, items have different importance. For this reason, weighted frequent pattern mining algorithms have been suggested that give different weights to items according to their significance. The main focus in weighted frequent pattern mining concerns satisfying the downward closure property.