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Analysis of Merchandise sales by factor, principal component and cluster models

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DOI: 10.23977/infse.2024.050119 | Downloads: 9 | Views: 163

Author(s)

Jiayi Wang 1, Chunhua Ji 1

Affiliation(s)

1 Zhonghua Vocational College of Ynufe, Yunnan University of Finance and Economics, Anning, 650399, China

Corresponding Author

Jiayi Wang

ABSTRACT

The correlation between different categories was determined by analyzing the six categories through factor analysis model, in which the correlation between cauliflower and leafy vegetables was the strongest, and the correlation between cauliflower and eggplant was the weakest. The method used in this paper can correlate the correlation between vegetables, and provide qualitative and quantitative analysis for the sales behavior between different vegetable dealers, and provide reference ideas and suggestions for their reasonable and effective operation. By considering the correlation between goods, their placement can be improved to result in a chain reaction of dish sales and improve overall sales. This can be achieved by placing complementary dishes together, allowing customers to directly purchase items that complement their meal. The goal is to achieve breakthroughs from a single product to the category.

KEYWORDS

Factor analysis, PCA, K-means clustering

CITE THIS PAPER

Jiayi Wang, Chunhua Ji, Analysis of Merchandise sales by factor, principal component and cluster models. Information Systems and Economics (2024) Vol. 5: 141-147. DOI: http://dx.doi.org/10.23977/infse.2024.050119.

REFERENCES

[1] LIN Liqian. Research on Cost Control of Fresh Food Retail Enterprises--Taking Supermarkets as an Example[J]. Mass Investment Guide, 2023(14):185-187. 
[2] Wang Zhijin. Using single product as a breakthrough to pry category growth[J]. China Drugstore, 2022(10):68-69. 
[3] Wang Yangxue, Jiao Jinyuan. Practical research on non-oil business operation system based on single product management [J]. Chemical Management, 2020(31):36-37. 
[4] Commentator. Innovative product "mutual sales" model[J]. Pivot Point, 2023(10):1. 
[5] Cang Chun. Parallel single product, the core play of sales improvement[J]. China Drugstore, 2022(09):32-33. 
[6] An Kui. Research on cooperative optimization of M fresh supermarket supply chain inventory [D]. Guizhou University, 2022. 
[7] J. K N, N. Z S, Jeffrey D N, et al. Detection of differential depressive symptom patterns in a cohort of perinatal women:an exploratory factor analysis using a robust statistics approach[J]. e Clinical Medicine, 2023, 57. 
[8] Zhang MX, Yang Y. Research on financial risk evaluation and control of listed enterprises in Heilongjiang province based on factor analysis [J]. Modern Auditing and Accounting, 2023(09):25-27. 
[9] YU Jing, JIANG Anlin, LIU Liang et al. Research on aerodynamic shape parameterization method based on PCA dimensionality reduction [J/OL]. Journal of Aeronautics: 1-17
[10] Chao M A , Sen C , Xiao-Bo L ,et al.Evaluation on simulative transportation and shelf quality of blueberries by different treatments based on principal component analysis[J].Food Science and Technology, 2018.
[11] JI Li, LIU Xiaoran, WU Qiang et al. Establishment of a prediction model for the first flowering period of waxberry flowers based on PCA [J]. Journal of Southwest Normal University (Natural Science Edition), 2022, 47(10):59-66. 
[12] Kang Glimmer. Research on subclassification of glass artifacts based on improved GDBT and K-means clustering [J]. Information Technology and Informatization, 2023(07):149-152. 
[13] SUN Lin, LIU Menghan. K-means clustering based on adaptive cuckoo optimized feature selection[J/OL]. Computer Applications: 1-13
[14] Wu Huihui, Yuan Zhe, Hui Xiaojian et al. Research analysis of Olympic awards based on K-means clustering[J]. Modern Information Technology, 2023, 7(15):136-140. 
[15] Yinbo Xu, Yang Yu. Detection of abnormal data in ship communication network based on K-means clustering[J]. Ship Science and Technology, 2023, 45(16):169-172. 

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