Approximate high utility itemset mining in noisy environments

High utility pattern mining has been proposed to overcome the limitations of frequent pattern mining which cannot reflect the unique profits of items. High utility pattern mining has been actively conducted because it can find more valuable patterns than previous fields of pattern mining. However, its traditional approaches are designed to perform on the assumption that the data stored in databases is faultless. If there are unknown errors, such as noises, in a given database, the mining results traditional high utility pattern mining approaches mined in this database cannot be fully trusted. In this paper, a novel technique considering the noises is suggested in order to overcome this limitation. The proposed technique calculates the ranges of trustworthy utilities for patterns using a utility tolerance factor. By using this factor, the robust high utility patterns, called as approximate high utility patterns, can be extracted from a noisy database. To evaluate the performance of the proposed algorithm, various experiments are designed and conducted in terms of runtime, memory usage, and scalability. The experimental results show that the proposed algorithm outperforms than competitors, an apriori-based approach and UP-Growth. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

A new technique for high utility pattern mining considering data noises is proposed in this paper, which calculates trustworthy ranges for patterns using a utility tolerance factor to extract robust high utility patterns from noisy databases. Experimental results demonstrate that the proposed algorithm outperforms competitors in terms of runtime, memory usage, and scalability.