High-utility-itemset mining, extended from frequent-itemset mining, considers external utilities of items in quantitative databases to obtain the itemsets with high utility. To easily understand those patterns with high utility values, fuzzy utility mining adopts fuzzy sets to increase the readability of the derived utility itemsets. However, real-world databases are usually dynamic. New transactions may be intermittently added, and the corresponding mined patterns must be updated to keep correct knowledge. In this paper, we propose an incremental method based on our previously proposed batch-processing fuzzy utility tree-based mining algorithm. The proposed approach adopts the fast-update (FUP) strategy. It considers newly coming data to readjust the head table and the tree structure, which generate desired itemsets in two phases. The experimental results reveal that the proposed algorithm outperforms the tree-based batch mining method.

Erasable itemset mining has been a valuable mining problem for manufacturers. It can extract less profitable materials from a product dataset and provide managers with good decision-making and a trade-off between cost and profit. However, the traditional erasable itemset mining methods seldom consider the time factor. For time-sensitive industries such as agro-processors, the time range is important in determining which materials are less profitable. Hong et al. first proposed the concept of temporal erasable itemset mining and seven lifespan options. They also proposed a unified temporal erasable (UTE) mining algorithm for getting incomplete temporal erasable itemsets. Howerever, the UTE algorithm does not satisfy the property of downward closure. In this work, we propose an improved algorithm to improve the performance of the UTE algorithm based on a lower-bound strategy and satisfying the property of downward closure. The proposed algorithm uses a hash table to store information that will be reused during the mining process to avoid scanning a dataset multiple times. The designed lower-bound strategy can preserve the downward closure property, narrowing the search space of candidate itemsets. In numerical experiments, we compare the performance using several metrics between the proposed method and the previous work. From the results of experiments, our proposed method outperforms the existing method on various metrics, such as execution time and the number of candidate erasable itemsets.

Temporal utility mining is complicated than utility mining due to the time consideration. The former discusses the situation that different items may have different profit values and on-shelf time periods. In this paper, we handle the problem of temporal fuzzy utility mining, which simultaneously considers the temporal factor, purchased quantities, item profits and linguistic terms from a transaction database. In the past, we proposed a two-phase tree-based approach to solve this problem. In this paper, we further design a tree structure with less memory to store all the required information and with faster execution speed than the previous one. The experimental results show that the proposed method can get good performance on the execution efficiency and consumed memory size.

More complicated than fuzzy data mining, temporal fuzzy utility data mining takes into account the temporal factor of transactions, purchased quantities, item profits, and linguistic terms. In this paper, a tree structure modified from the frequent-pattern tree is designed and a mining algorithm based on it was proposed to extract high temporal fuzzy utility patterns from transactional datasets with the temporal property. The method requires two-phase processing to find all high temporal fuzzy utility itemsets. Experimental results show that the proposed algorithm performs better than the Apriori-based mining algorithm.