|Name||Infrequent Weighted Itemset Mining Using Frequent Pattern Growth|
|Technology||Dot net, MS SQL|
|Description||Frequent weighted itemsets represent correlations
frequently holding in data in which items may weight
differently. However, in some contexts, e.g., when the
need is to minimize a certain cost function, discovering
rare data correlations is more interesting than mining
frequent ones. This paper tackles the issue of
discovering rare and weighted itemsets, i.e., the
infrequent weighted itemset (IWI) mining problem.
Two novel quality measures are proposed to drive the
IWI mining process. Furthermore, two algorithms that
perform IWI and Minimal IWI mining efficiently,
driven by the proposed measures, are presented.
Experimental results show efficiency and effectiveness
of the proposed approach.
|IEEE Paper Year||2014|