Application of Sequential Pattern Mining Algorithm in Commodity Management

Abstract

This article describes how by analyzing historical sales data of supermarkets, once data is cleaned, sampled and put through a series of operations, it can be transformed into a sequence database. Finally, the data is used in the SPM of Map-Reduce algorithm to data mining. This experiment has two stages. In the first stage, the sequences of product categories are mined to place the product categories. In the second stage, the products are mined for each category, add profit targets for calculating sequential pattern values. This is so that it can reorder the results to find the most profit products. Thus, readers can adjust products placement and improve the profits of the supermarket.

Construction and Effect Analysis of College Students’ Physical Education Teaching Mode Based on Data Mining Algorithm

“…Wang et al adopted the classification method of DT (decision tree) [19], applied DM technology to students' achievement information, and constructed the DT model of professional ability, which helped teachers to gain a more accurate and efficient insight into the existing problems in the teaching process, and achieved the effect of optimizing teaching quality by using achievement information.…”

“…The smaller the coefficient of variation, the higher the coordination degree. If the coefficient of variation is greater than 0.25, it is considered that the degree of coordination is not high [19,20]. The calculation formula is where V j represents the coefficient of variation, S j represents the standard deviation, and M j represents the arithmetic mean.…”

Multi-Objective Design of Profit Volumes and Closeness Ratings Using MBHS Optimizing Based on the PrefixSpan Mining Approach (PSMA) for Product Layout in Supermarkets

“…The authors of [59] proposed an analysis method based on the Poisson model that considers impactable parameters, such as the time of the day, day of the week, and week of the month, to investigate a source of variation in a case study from Chile. The authors of [37] proposed the use of the Map-Reduce algorithm in conjunction with sequential pattern mining to find correlations between groups of products. The data were randomized for sequential pattern mining with the aim of finding the most profitable products in order to improve the overall profitability of a supermarket.…”

“…To deal with this critical problem, a PrefixSpan algorithm [32][33][34] has been implemented. Prior to this algorithm, authors [35][36][37][38] paid attention to the management of the location of each specific shelf in a supermarket. Although this method is able to increase sales, it does not consider possible correlations between products on different shelves by analyzing customers' shopping lists, a gap that could lead to crucial sales losses.…”