Using Tree Structures for Maintenance of High Fuzzy Utility Itemsets

In this big-data era, data mining is beneficial in many industries. Given the important role of each item’s utility value, utility mining has become more and more attractive in many fields. Applied to data mining methods, fuzzy-set theory makes mined results easier to understand by using linguistic terms. However, data usually arrive intermittently, and users may want to access the current mined results at any time, not just after all the data have been collected. This work thus focuses on utility mining in incremental databases using tree structures. We propose a maintenance approach for high fuzzy utility itemsets based on pre-large itemsets and upper bounds to shorten execution time. We also compare the proposed maintenance approach with our previous approach based on the FUP strategy as well as one based on batch re-mining. Experimental results show the proposed pre-large-based algorithm yields the shortest execution time of the three tree-based approaches.