Mining periodic high-utility itemsets with both positive and negative utilities

Mining high-utility patterns in databases containing items with both positive and negative profits is useful in market basket databases, since negative profits are common in the real world. Obviously, in the market basket database, patterns with stable long-term profits have more meaning. The discovery of itemsets with a consistent high frequency is known as periodic frequent pattern mining. Therefore, mining periodic high-utility patterns in a database containing items with both positive and negative profits is an interesting and useful task. However, this task has two main challenges. First, the utility measure does not have the download closure property. Second, the huge search space needs to be pruned more effectively. In this paper, we propose a vertical data structure-based algorithm called PHMN to discover periodic high-utility patterns (PHUPs) or itemsets in a transaction database with both positive and negative utilities. To be more efficient, we propose a new upper bound to prune the search space and an improved algorithm to discover the PHUPs. Finally, experiments are conducted to verify the effectiveness and efficiency of algorithms.

Automated summary

This paper investigates the mining of high-utility patterns in databases with items of positive and negative profits, which is useful in market basket databases due to the common existence of negative profits in the real world. The discovery of itemsets with consistently high frequency is referred to as periodic frequent pattern mining. However, two main challenges exist in this task: the lack of download closure property in the utility measure and the need to effectively prune the huge search space. To address these challenges, the authors propose a vertical data structure-based algorithm called PHMN, which efficiently discovers periodic high-utility patterns (PHUPs) or itemsets in transaction databases with positive and negative utilities. Experimental results are provided to validate the effectiveness and efficiency of the proposed algorithms.