Mining of infrequent itemset From Transactional Weighted Datasets Using Frequent Pattern Growth
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 4
Abstract
Itemset mining is a data mining method extensively used for learning important correlations among data. Initially item sets mining was made on discovering frequent itemsets. Frequent weighted item set characterizes data inwhich items may weight differently through frequent correlations in data’s. But, in some situations, for instance certaincost functions need to be minimized for determining rare data correlations.In recent years, the thoughtfulness of the research community has also been focused on the infrequent itemset mining problem, i.e., discovering itemsets whose frequency of occurrence in the analyzed data is less than or equal to a maximum threshold. This grind addresses the discovery of infrequent and weighted itemsets, i.e., the infrequent weighted itemsets, from transactional weighted data sets. To speech this issue, the IWI-support measure is defined as a weighted, frequency of occurrence of an itemset in the analyzed data. Occurrence weights are derived from the weights associated with items in each transaction by applying a given cost function. In particular, we focus our attention on two different IWI-support measures: (i) The IWI-support- min measure, (ii) The IWI-support-max measure. Furthermore, two algorithms that perform IWI and Minimal IWI mining efficiently, driven by the proposed measures, are presented.
Authors and Affiliations
J. Jaya, S. V. Hemalatha
Keywords
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