An efficient algorithm for mining high utility itemsets with negative item values in large databases

Utility itemsets typically consist of items with different values such as utilities, and the aim of utility mining is to identify the itemsets with highest utilities. In the past studies on utility mining, the values of utility itemsets were considered as positive. In some applications, however, an itemset may be associated with negative item values. Hence, discovery of high utility itemsets with negative item values is important for mining interesting patterns like association rules. In this paper, we propose a novel method, namely HUINIV (High Utility Itemsets with Negative Item Values)-Mine, for efficiently and effectively mining high utility itemsets from large databases with consideration of negative item values. To the best of our knowledge, this is the first work that considers the concept of negative item values in utility mining. The novel contribution of HUINIV-Mine is that it can effectively identify high utility itemsets by generating fewer high transaction-weighted utilization itemsets such that the execution time can be reduced substantially in mining the high utility itemsets. In this way, the process of discovering all high utility itemsets with consideration of negative item values can be accomplished effectively with less requirements on memory space and CPU I/O. This meets the critical requirements of temporal and spatial efficiency for mining high utility itemsets with negative item values. Through experimental evaluation, it is shown that HUINIV-Mine outperforms other methods substantially by generating much less candidate itemsets under different experimental conditions.