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