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 item-sets. 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. Mining of association rules in large databases is a well studied technique in the field of data mining with typical methods like Apriori [1,2]. The problem surrounding association rules mining can be decomposed into two steps. The first step involves finding all frequent itemsets (or large itemsets) in a database. Once the frequent itemsets are found, generating association rules is straightforward and can be accomplished in linear time. Most methods in finding frequent itemsets are designed for traditional databases. However, the frequency of an itemset may not be a sufficient indicator of significance, because frequency reflects only the number of transactions in the database that contain that itemset. It does not reveal the utility of an itemset, which can be measured in terms of cost, profit, or other expressions of user preference. On the other hand, frequent itemsets may only contribute a small portion of the overall profit, whereas non-frequent itemsets may contribute a large portion of the profit. In reality, a …