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. In this research, frequent pattern mining approaches with weight constraints are
suggested. Our main approach is to push weight constraints into the pattern growth
algorithm while maintaining the downward closure property. We develop WFIM
(Weighted Frequent Itemset Mining with a weight range and a minimum weight),
WLPMiner (Weighted frequent Pattern Mining with length decreasing constraints), WIP
(Weighted Interesting Pattern mining with a strong weight and/or support affinity),
WSpan (Weighted Sequential pattern mining with a weight range and a minimum
weight) and WIS (Weighted Interesting Sequential pattern mining with a similar level of
support and/or weight affinity)
The extensive performance analysis shows that suggested approaches are
efficient and scalable in weighted frequent pattern mining.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/5003 |
| Date | 25 April 2007 |
| Creators | Yun, Unil |
| Contributors | Leggett, John J. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 1626762 bytes, electronic, application/pdf, born digital |
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