Incremental high average-utility itemset mining: survey and challenges

TL;DR

This paper reviews incremental high average utility itemset mining (iHAUIM), discussing its techniques, advantages, challenges, and future research directions in dynamic database environments.

Contribution

It provides a comprehensive survey of iHAUIM algorithms, categorizing methods and analyzing their characteristics, which is a novel synthesis in this specific research area.

Findings

Categorization of iHAUIM algorithms into Apriori-based, Tree-based, and Utility-list-based methods.

Critical analysis of the advantages and disadvantages of each iHAUIM technique.

Identification of future research directions and potential extensions in iHAUIM.

Abstract

The High Average Utility Itemset Mining (HAUIM) technique, a variation of High Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and business decision-making necessitate regular updates of the database with new transactions. As a result, researchers have developed incremental HAUIM (iHAUIM) algorithms to identify HAUIs in a dynamically updated database. Contrary to conventional methods that begin from scratch, the iHAUIM algorithm facilitates incremental changes and outputs, thereby reducing the cost of discovery. This paper provides a comprehensive review of the state-of-the-art iHAUIM algorithms, analyzing their unique characteristics and advantages. First, we explain the concept of iHAUIM, providing formulas and real-world examples for a more in-depth understanding. Subsequently, we categorize and discuss the key technologies used by varying types of iHAUIM algorithms, encompassing Apriori-based, Tree-based, and Utility-list-based techniques. Moreover, we conduct a critical analysis of each mining method's advantages and disadvantages. In conclusion, we explore potential future directions, research opportunities, and various extensions of the iHAUIM algorithm.