Mining Significant Utility Discriminative Patterns in Quantitative Databases

Huijun Tang, Jufeng Wang () and Le Wang
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Huijun Tang: Faculty of Finance and Information, Ningbo University of Finance & Economics, Ningbo 315175, China
Jufeng Wang: Faculty of Finance and Information, Ningbo University of Finance & Economics, Ningbo 315175, China
Le Wang: Faculty of Digital Technology and Engineering, Ningbo University of Finance & Economics, Ningbo 315175, China

Mathematics, 2023, vol. 11, issue 4, 1-18

Abstract: Drawing a discriminative pattern in quantitative datasets is often represented to return a high utility pattern (HUP). The traditional methods output patterns with a utility above a pre-given threshold. Nevertheless, the current user-centered algorithm requires outputting the results in a timely manner to strengthen the interaction between the mining system and users. Pattern sampling can return results with a probability guarantee in a short time, and it could be a candidate technology to mine such discriminative patterns. In this paper, a novel approach named HUPSampler is proposed to sample one potential HUP, which is extracted with probability significance according to its utility in the database. HUPSampler introduces an interval constraint on the length of HUP and randomly extracts an integer k according to the utility proportion firstly; then, the HUPs could be obtained efficiently from a random tree by using a pattern growth way, and finally, it returns a HUP of length k randomly. The experimental study shows that HUPSampler is efficient in regard to memory usage, runtime, and utility distribution. In addition, case studies show that HUPSampler can be significantly used in analyzing the COVID-19 epidemic by identifying critical locations.

Keywords: high utility pattern; sampling; quantitative database; COVID-19 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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