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Surapong Uttama. Enhancing data collection for market basket analysis through CNN object detection. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2024.
Enhancing data collection for market basket analysis through CNN object detection
Name: Thandar Phyo
Organization :
Mae Fah Luang University. School of Information Technology
Email :
thandarphyo720@gmail.com
Name: Surapong Uttama
Organization :
Mae Fah Luang University. School of Information Technology
Email :
surapong@mfu.ac.th
keyword:
Market basket analysis
LCSH:
Consumer behavior
-- Data processing
Abstract:
Analyzing data from night markets, particularly
for market basket analysis, poses significant challenges.
Previous studies collected images of products from customers at
these markets, yet these images often depicted food items and
products in plastic bags or boxes, complicating identification. To
address this issue, our study utilizes object detection techniques
to automatically label food items and products, thereby
enhancing the accuracy and efficiency of market basket
analysis. We evaluate the effectiveness of these techniques using
standard evaluation metrics such as Precision-Recall curves and
Average Precision (AP). Our results demonstrate commendable
performance, with a mean Average Precision (mAP) score of
99.4% for food items without plastic bags and 99.3% for those
with plastic bags. Notably, the combined model, capable of
detecting both types of food items, achieves an mAP of 84.4%.
Additionally, we utilized three association rule learning
algorithmsApriori, FP-Growth, and Eclat for market basket
analysis to uncover meaningful associations among food
categories. Among these algorithms, the Apriori algorithm
produced the highest support value of 20% and confidence of
50%, generating a total of 8 rules. The accuracy of these
association rules on a new dataset, comprising 20 transactions,
is calculated to be 84%. These findings offer actionable insights
for businesses in the food industry, empowering them to tailor
marketing strategies and product offerings to better align with
consumer needs and preferences. Ultimately, our study
contributes to a deeper understanding of consumer behavior
and product associations in the food industry, paving the way
for future research endeavors in this domain.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2024
Modified:
2025-05-26
Issued:
2025-05-26
บทความ/Article
application/pdf
BibliograpyCitation :
In IEEE Thailand Section (IEEE Computer Society Thailand Chapter) and Prince of Songkla University. College of Computing. The 21st International Joint Conference on Computer Science and Software Engineering (JCSSE 2024)) (pp.302-309). Phuket : Prince of Songkla University
eng
©copyrights King Mongkut's University of Technology North Bangkok
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Thandar Phyo
| Title | Contributor | Type |
|---|---|---|
| Enhancing data collection for market basket analysis through CNN object detection
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Thandar Phyo;Surapong Uttama | บทความ/Article |
Surapong Uttama
| Title | Contributor | Type |
|---|---|---|
| Development of IoT fall detection system
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Wachirawat Jaratmayteenon;Suparat Panya;Ronnachai Sretawat Na Ayutaya;Mahamah Sebakor;Surapong Uttama;Suppakarn Chansareewittaya | บทความ/Article | |
| Effectiveness of classifying unsafe children\'s toys using NLP, deep learning and ensemble learning
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Wai, Htoo Thiri;Surapong Uttama | บทความ/Article | |
| Enhancing data collection for market basket analysis through CNN object detection
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Thandar Phyo;Surapong Uttama | บทความ/Article | |
| Enhancing money laundering detection addressing imbalanced data and leveraging typological features analysis
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ The, Hnin Phyu;Surapong Uttama | บทความ/Article |