Affiliations: [a] Department of Management Information Systems, Central Taiwan University of Science and Technology, Taichung, Taiwan | [b] Department of Business Administration, National Chung Cheng University, Min-Hsiung, Chia-Yi, Taiwan
Correspondence:
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Corresponding author: Tony Cheng-Kui Huang, Department of Business Administration, National Chung Cheng University, 168, University Rd., Min-Hsiung, Chia-Yi, Taiwan. Tel.: +886 5 2729376; Fax: +886 5 2720564; E-mail:bmahck@ccu.edu.tw
Abstract: It is crucial for businesses to identify any revenue bottlenecks generated
from itemsets (also called products). For improving revenue, business
managers must determine itemsets that should be retained in or eliminated
from shops. Therefore, identifying itemsets with higher values (utility and
cost-benefit) from frequent itemsets with low utility is appealing. However, past
studies on utility itemset mining have focused on high-utility itemsets
rather than low-utility itemsets. The current study thus proposes a new
algorithm to discover frequent itemsets with low utility. Experimental
results obtained using two real-life datasets show that the top k itemsets
determined by the proposed algorithm have higher prediction performance in
the measures of utility and cost-benefit compared with other frequent
itemsets determined by a traditional algorithm.
