Incremental High Fuzzy Utility Itemset Mining
Tzung‐Pei Hong, Wei-Teng Hung, Wei-Ming Huang, Yu‐Chuan Tsai · 2022
In data mining, frequent-pattern mining methods are used for handling binary databases. Utility mining addresses this limitation by considering the item utilities and quantities when discovering the high utility itemsets. In addition, to make these high utility patterns more human-explainable, we use the fuzzy-set theory to the utility mining algorithms. However, real-world databases are usually dynamic. That is, new transactions may be intermittently added, and the corresponding mined knowledge needs to be updated. This paper uses the famous incremental strategy, fast update (FUP), to modify high fuzzy utility itemsets from new coming data. We implement the FUP strategy on the Apriori-based approach for maintaining up-to-date high fuzzy utility itemsets.We also conducted experiments to demonstrate that the incremental algorithm significantly outperforms the Apriori-based batch mining method.