A fuzzy approach for mining high utility quantitative itemsets
Chia-Ming Wang, Shyh‐Huei Chen, Yin‐Fu Huang · 2009
Studies on frequent pattern mining have often considered the existence of an item in a transaction but ignored its profit and purchase quantity. As a result, utility mining has been introduced to resolve this issue. However, the resulting patterns simply show the correlation among high utility items without quantity information of such items. In this study, a novel method is proposed to discover high utility fuzzy itemsets from quantitative databases which considers both profits and quantities of items. The quantities are fuzzified into linguistic regions, thus the quantitative concept is consistent with human cognition and easy to interpret. In addition, we provide theoretical and empirical analysis of the proposed method. The simulation results demonstrated that our method can efficiently and effectively mine high utility fuzzy itemsets from quantitative databases.