Issue No.04 - April (2014 vol.26)
Luca Cagliero , Dipt. di Autom. e Inf., Politec. di Torino, Turin, Italy
Paolo Garza , Dipt. di Autom. e Inf., Politec. di Torino, Turin, Italy
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.69
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.
data mining, Clustering, classification, and association rules,
Luca Cagliero, Paolo Garza, "Infrequent Weighted Itemset Mining Using Frequent Pattern Growth", IEEE Transactions on Knowledge & Data Engineering, vol.26, no. 4, pp. 903-915, April 2014, doi:10.1109/TKDE.2013.69