Minimal Infrequent Weighted Item Set Mining
Frequent item set mining leads to the discovery of associations and correlations among item sets in large
transactional data base. Weighted pattern mining can discover more important knowledge compared to the traditional
frequent pattern mining by considering different weights of the items. It plays an important role in the real world scenarios.
The main contribution of the weighted frequent pattern mining is to retrieve this hidden knowledge from database. In recent
years, the attention of the research community has also been focused on the infrequent item set mining problem, i.e.,
discovering item sets whose frequency of occurrence in the analyzed data is less than or equal to a maximum threshold. This
paper addresses the discovery of infrequent and weighted item sets, i.e., the Infrequent Weighted Item sets (IWIs), from
transactional weighted datasets. To address this issue, the IWI-support measure is defined as a weighted frequency of
occurrence of an item set in the analyzed data. Occurrence weights are derived from the weights associated with items in
each transaction by applying a given cost function.
Keywords: Frequent item set, Infrequent item set, weighted item set, Minimal infrequent item set, Equivalence Property.