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Journal articles on the topic 'Market basket analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Harini, J. Venkata, G. Venu, G. Vijay Kiran Reddy, et al. "Market Basket Analysis." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5224–29. http://dx.doi.org/10.22214/ijraset.2024.61662.

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Abstract: Market Basket analysis is a technique applied by retailers to understand customer’s shopping behaviour from their stores. The result of the effective analysis may improve supplier’s profitability, quality of service and customer satisfaction. The purpose of this project is to make use of anonymized data on customers’ transactional orders to focus on descriptive analysis on the customer purchase patterns, items which are bought together and units that are highly purchased from the store to facilitate reordering and maintaining adequate product stock. Market Basket Analysis is an impor

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2

Kamakura, Wagner A. "Sequential market basket analysis." Marketing Letters 23, no. 3 (2012): 505–16. http://dx.doi.org/10.1007/s11002-012-9181-6.

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3

Atul Sharma, Mohammad Salim Hamidi, and Yousuf Hotak. "MARKET BASKET ANALYSIS USING MACHINE LEARNING." International Journal of Computer Science & Communications (IJCSC) 9, no. 1 (2024): 14–21. http://dx.doi.org/10.58885/ijcsc.v09i1.014.as.

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The test database products that are most likely to be purchased together, primarily in the retail and economic sectors, are analyzed using the "market basket analysis" method. This method works particularly well for optimization. We used the market basket dataset from the Kaggle library for our research study. Using the Python programming language, this test database was examined for the well-known management subject of "Market Basket Analysis."

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A., Izang, Okoro U., Olarewaju T., Fasanu T., and Adeyinka A. "Automated Market Basket Analysis System." International Journal of Computer Applications 180, no. 39 (2018): 44–51. http://dx.doi.org/10.5120/ijca2018917043.

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5

Raeder, Troy, and Nitesh V. Chawla. "Market basket analysis with networks." Social Network Analysis and Mining 1, no. 2 (2010): 97–113. http://dx.doi.org/10.1007/s13278-010-0003-7.

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Mooy, Gisela Christy, and Sani M. Isa. "Contextual Market Basket Analysis during Covid-19." Journal of Social Science 4, no. 3 (2023): 815–25. http://dx.doi.org/10.46799/jss.v4i3.577.

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    One form of Data Mining application to analyze Market Basket Analysis. Market Basket Analysis helps identify buying patterns formed from concurrent transactions. One of the problems with Market Basket Analysis is that customer needs vary according to season and time of day, especially during this covid-19 season. For this purpose, by using the Artificial Neural Network (ANN) Approach that is connected to Market Basket Analysis, it can analyze and compare purchasing patterns and can identify rules that were formed before and after covid-19; several rule changes were

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Śniegocka - Łusiewicz, Małgorzata. "Market basket analysis in marketing research." Equilibrium 2, no. 1 (2009): 115–23. http://dx.doi.org/10.12775/equil.2009.011.

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Market basket analysis is, to put it simply, analysis of the content of customer’s trolley. Market Basket Analysis is based on recognition of rules that customers apply while filling their trolleys, their habits, proper use of services and on research in what products are purchased together or in a defined order. Identification of the aforementioned rules may have a great influence on increasing customer’s value. Basket analysis may be applied for planning promotional campaigns, verifying their effectiveness, effective offering additional services and optimising service packages, tariffs and f

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Valarmathi, Dr A. "Market Basket Analysis for Mobile Showroom." International Journal for Research in Applied Science and Engineering Technology V, no. X (2017): 1279–84. http://dx.doi.org/10.22214/ijraset.2017.10185.

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Vahidi Roodpishi, Marzieh, and Reza Aghajan Nashtaei. "Market basket analysis in insurance industry." Management Science Letters 5, no. 4 (2015): 393–400. http://dx.doi.org/10.5267/j.msl.2015.2.004.

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10

Santarcangelo, Vito, Giovanni Maria Farinella, Antonino Furnari, and Sebastiano Battiato. "Market basket analysis from egocentric videos." Pattern Recognition Letters 112 (September 2018): 83–90. http://dx.doi.org/10.1016/j.patrec.2018.06.010.

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11

Dippold, Katrin, and Harald Hruschka. "Variable selection for market basket analysis." Computational Statistics 28, no. 2 (2012): 519–39. http://dx.doi.org/10.1007/s00180-012-0315-3.

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12

Woo, Jongwook. "Market Basket Analysis algorithms with MapReduce." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, no. 6 (2013): 445–52. http://dx.doi.org/10.1002/widm.1107.

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13

Lawrence, Edgar, Bagus Mulyawan, and Novario Jaya Perdana. "MARKET BASKET ANALYST BASED ON WEBSITE USING ECLAT ALGORITHM (CASE STUDY POLA PHARMACY)." Jurnal Ilmu Komputer dan Sistem Informasi 8, no. 2 (2020): 205. http://dx.doi.org/10.24912/jiksi.v8i2.11497.

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Market basket analyst is a data mining technique todiscover associations between datasets. Association rulemining identifies a relationship between a large set ofdata item. When a large quantity of data is constantlyobtained and stored in database, several industries arebecoming concerned about mining association rulesfrom their databases. Market basket analysis examinescustomer buying patterns by identifying associationsamong various items that customers place in theirshopping baskets. It is helpful to examine customerpurchasing behaviour and assists in increasing sales. Sothis system is inte

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Katarina Sianturi, Susy, Dina Satriani Fansuri, and Wiwin Najmiatul Aini. "Algoritma Apriori untuk Mengetahui Pola Beli Konsumen Pada Sistem Informasi Market Basket Analysis Berbasis Andriod." Jurnal Insan Unggul 11, no. 1 (2023): 35–58. http://dx.doi.org/10.47926/jiu.2023.11.1.35-58.

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Perubahan pola beli konsumen yang terjadi pada Toko Sembako Siger mengakibatkan penumpukan suatu barang pada toko sehingga pemilik toko ragu untuk membeli ulang suatu barang dikarenakan kombinasi barang yang dibeli oleh konsumen berubah-ubah. Oleh sebab itu, dilakukan market basket analysis untuk mengetahui pola beli konsumen sehingga pemilik toko dapat mengambil keputusan dari hasil market basket analysis. Dalam penerapan market basket analysis untuk mengetahui pola beli konsumen digunakan sistem informasi berbasis android yang mengimplementasi teknik data mining yaitu association rule dengan

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Nidhi, Makarand Kawale, and Snehil Dahima Dr. "Market Basket Analysis using Apriori Algorithm in R Language." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 2628–33. https://doi.org/10.31142/ijtsrd15677.

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Market Basket Analysis is a data processing technique that is used in the discovery of relations among various items. The main purpose of market basket analysis in retail is to provide information to the distributor to know the buying behaviour of a customer, which can help the distributor in creating the right selections. There are various algorithms available for performing market basket analysis. This paper discusses the data mining technique i.e. association rule mining which may be helpful to examine the customer purchasing behaviour and assists in increasing the sales. Results can provid

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Kawale, Nidhi Makarand, and Dr Snehil Dahima. "Market Basket Analysis using Apriori Algorithm in R Language." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 2628–33. http://dx.doi.org/10.31142/ijtsrd15677.

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17

Adi, Ahmad Cahyono. "COMPARATIVE STUDY OF FP - GROWTH AND APRIORI IN GROCERY ANALYSIS." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 11, no. 1 (2024): 101–8. https://doi.org/10.33330/jurteksi.v11i1.3164.

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Abstract: The growing business environment causes the business world to create in order to continue to survive, one of which is by increasing sales. One way is to use the data approach. One method of data approach that is widely used is Market Basket Analysis. This study uses the Market Basket Analysis method with the a priori algorithm and FP-Growth. Grocery dataset analysis using two algorithms, Apriori and FP-Growth using a minimum support parameter of 0.45, has results sorted by the top 10 associations with the best confidence value. The association with the highest support value found is

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18

Rezende, Felipe, and Marcelo Ladeira. "Market Basket Analysis in a Financial Institution." Revista Singular - Engenharia, Tecnologia e Gestão 1, no. 1 (2019): 6–12. http://dx.doi.org/10.33911/singular-etg.v1i1.18.

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This article demonstrates a study on Market Basket Analysis of a financial institution, showing rules of personal consumer association of the state of São Paulo. A concept about three association algorithms is presented, but a study with only one is performed. The paper is divided into an introduction, describing a brief account of the reason for choosing the subject. Understanding the business, where it is explained about the financial institution and the importance of the study to the institution. The way the data are handled is demonstrated in Understanding the Data, just as the Data Prepar

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Priyadarshi, Abhishek, Chirag Gupta, and G. Poornalatha. "Market Basket Analysis using Improved FP-tree." International Journal of Data Mining Techniques and Applications 5, no. 2 (2016): 110–13. http://dx.doi.org/10.20894/ijdmta.102.005.002.002.

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20

FAHRUDIN, NUR FITRIANTI. "Penerapan Algoritma Apriori untuk Market Basket Analysis." MIND Journal 4, no. 1 (2019): 13–23. http://dx.doi.org/10.26760/mindjournal.v4i1.13-23.

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Pemanfaatan teknik data mining saat ini dapat membantu para pemilik bisnis untukmeningkatkan penjualan produk mereka. Salah satu teknik yang sangat dikenaladalah analisis asosiasi. Analisis asosiasi bertujuan untuk menemukan hubunganantara barang-barang yang dibeli oleh pelanggan. Analisis asosiasi semacam ini biasadikenal sebagai market basket analysis. Market basket analysis menggunakan datapelanggan yang selama ini disimpan didalam basis untuk menemukan informasi barudidalamnya. Algoritma Apriori merupakan algoritma untuk melakukan market basketanalisis, yang bertujuan untuk menemukan baran

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21

Aguinis, Herman, Lura E. Forcum, and Harry Joo. "Using Market Basket Analysis in Management Research." Journal of Management 39, no. 7 (2012): 1799–824. http://dx.doi.org/10.1177/0149206312466147.

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22

Monteserin, Ariel, and Marcelo G. Armentano. "Influence-based approach to market basket analysis." Information Systems 78 (November 2018): 214–24. http://dx.doi.org/10.1016/j.is.2018.01.008.

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23

Xu, Yaquan, and Manying Qiu. "A GRAPHICAL ANALYSIS FOR MARKET BASKET DATA." European Journal of Business Research 13, no. 2 (2013): 11–16. http://dx.doi.org/10.18374/ejbr-13-2.2.

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Aguinis, Herman, Lura E. Forcum, and Harry Joo. "Using Market Basket Analysis in Management Research." Academy of Management Proceedings 2012, no. 1 (2012): 10152. http://dx.doi.org/10.5465/ambpp.2012.10152abstract.

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25

Moodley, Raymond, Francisco Chiclana, Fabio Caraffini, and Jenny Carter. "Application of uninorms to market basket analysis." International Journal of Intelligent Systems 34, no. 1 (2018): 39–49. http://dx.doi.org/10.1002/int.22039.

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26

Neupane, Ashish, Bibek Dhakal, Bijay Aryal, and Nabin Kandel. "Identifying Product Bundle from Market Basket Analysis." International Journal on Engineering Technology 1, no. 1 (2023): 129–38. http://dx.doi.org/10.3126/injet.v1i1.60935.

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Bundling has emerged as an increasingly popular promotional strategy, offering numerous benefits to buyers and sellers and aligning perfectly with the goals of a transaction process. From the consumer’s perspective, bundling enables them to enjoy substantial savings, with an average percentage off, when purchasing a bundle package at a discount price. This significant cost reduction serves as a critical motivator for embracing bundling. Furthermore, bundling allows customers to streamline their purchasing experience by minimizing search costs. They can conveniently find all the desired product

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Anwar Arifin, Indra Wahyudi,. "PERBAIKAN TATA LETAK PASAR INDUK TRADISIONAL DI SANGATTA DENGAN METODE MARKET BASKET ANALYSIS (MBA)." Research Journal of Accounting and Business Management 5, no. 2 (2022): 113. http://dx.doi.org/10.31293/rjabm.v5i2.5784.

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ABSTRACTThe customer is one of the goals of the market. One of the factors that influence consumer satisfaction is the arrangement of products/products. An attractive display becomes the face of the market itself. The Traditional Main Market in Sangatta is a market that is satisfied with consumers. So far, the product arrangement of the Traditional Main Market in Sangatta has only been based on the intuition of the market management, while the wide market area causes consumers to feel tired when shopping.The Market Basket Analysis method aims to determine what products are usually purchased by

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Karim, Zahidul, Sumaya Fatema Binte Shahid, and Shahpar Shams. "Measuring the Impacts of Consumers’ Demographics on Spending Propensity and Expenses on Firms Profitability using Market Basket Optimization Model." International Journal of Business & Economics (IJBE) 7, no. 2 (2022): 173–90. http://dx.doi.org/10.58885/ijbe.v07i2.173.zk.

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Market Basket Analysis (MBA) and its importance in selecting the right basket of goods have obtained considerable interest among the managers and executives of many retail stores. The present study has explored the literature gap in measuring the determinants for managing the optimum market basket of goods for consumers. The study found that some customer demographic and firm specific expense variables provide important prediction power to measure the customer spending propensity and firms’ profitability respectively. We have used two different market basket optimization models to measure and

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Gupta, Khushi, Kashyapi Shah, and Ameya A. Kadam. "E-Commerce Market Basket Analysis using Apriori Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25922.

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This paper presents the usage of the Apriori Algorithm to implement market basket analysis to identify purchase patterns for items that are frequently bought together by customers. The results of this analysis are primarily used to improve sales of multi-product stores by enhancing product placement based on consumers’ shopping habit. In this particular scenario, we have used the data from an online E- commerce store that caters to customers across the world, but primarily focused to the United Kingdom. Key Words: Market Basket, Apriori Algorithm, Association Rules, E-Commerce, Consumer Behavi

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Elisa, Erlin. "Market Basket Analysis Pada Mini Market Ayu Dengan Algoritma Apriori." Edik Informatika 4, no. 1 (2018): 29–39. http://dx.doi.org/10.22202/ei.2017.v4i1.2641.

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Elisa, Erlin. "Market Basket Analysis Pada Mini Market Ayu Dengan Algoritma Apriori." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 2, no. 2 (2018): 472–78. http://dx.doi.org/10.29207/resti.v2i2.280.

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Data mining merupakan teknik untuk menggali informasi baru dari gudang data, informasi dipandang sangat penting dan berharga karena dengan menguasai informasi maka dengan mudah untuk mencapai sebuah tujuan, hal ini membuat setiap orang berlomba untuk memperoleh informasi, demikian juga pada usaha perdagangan seperti minimarket Ayu di Kota Batam. Minimarket ini berlokasi dekat dengan rumah penduduk, hal ini tentunya mempengaruhi tingkat penjualan, dengan adanya kegiatan penjualan setiap hari, data transaksi penjualan akan terus bertambah, menyebabkan penyimpanan data semakin besar. Data transak

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Idris, Andi Ilhamsyah, Eliyah A. M. Sampetoding, Valian Yoga Pudya Ardhana, et al. "Comparison of Apriori, Apriori-TID and FP-Growth Algorithms in Market Basket Analysis at Grocery Stores." IJICS (International Journal of Informatics and Computer Science) 6, no. 2 (2022): 107. http://dx.doi.org/10.30865/ijics.v6i2.4535.

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Market Basket Analysis is an analysis of consumer behavior specifically from a certain group/group. Market Basket Analysis is generally used as a starting point for seeking knowledge from a data transaction when we do not know what specific pattern we are looking for. Market Basket Analysis in this study is applied to the search for patterns of purchasing groceries at grocery stores and then analyzed by season. This study aims to compare the Apriori, Apriori TID and FP-Growth methods in determining consumer transaction behavior and calculating the quantity of consumer transactions in several s

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Widodo, Anang Aris, and Ighfir Azam Fachrudin. "PREDIKSI ITEMSET PROMOSI PENJUALAN MENGGUNAKAN METODE MARKET BASKET ANALYSIS." KONVERGENSI 18, no. 1 (2022): 18–24. http://dx.doi.org/10.30996/konv.v18i1.5645.

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ABSTRACT Every company always needs office stationery in completing its operational activities. Office stationery means items used to do paperwork. The photocopy of Nias Rama is a part of meeting the supply needs. The purpose of this study is to present the implementation of the Market Basket Analysis method using a web-based Apriori algorithm to create a system that can help quickly and precisely to see the association between the sale of goods with one another. The results of the Apriori algorithm calculations that have been carried out with a minimum of 2% Support and 60% Confidence g

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Zahedi, Zahedi, and Charies Chandra. "Perancangan Program Aplikasi Market Basket Analysis untuk Mendukung Persediaan Barang dengan Metode Fuzzy C-Covering." ComTech: Computer, Mathematics and Engineering Applications 3, no. 1 (2012): 241. http://dx.doi.org/10.21512/comtech.v3i1.2410.

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Any difficulty in analyzing sales transaction data is often faced by a company due to the huge number of sales transactions and the limited tools to process the data. It results in losses for the company since it is difficult to estimate the goods supply for subsequent sales. In this paper a data mining application is designed to analyze sales data. Market Basket Analysis is a data mining application that aims to determine most purchased or used products at once by the consumer. The process of Market Basket Analysis is to analyze consumer buying habits by finding associations among products pu

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Musalem, Andres, Luis Aburto, and Maximo Bosch. "Market basket analysis insights to support category management." European Journal of Marketing 52, no. 7/8 (2018): 1550–73. http://dx.doi.org/10.1108/ejm-06-2017-0367.

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Purpose This paper aims to present an approach to detect interrelations among product categories, which are then used to produce a partition of a retailer’s business into subsets of categories. The methodology also yields a segmentation of shopping trips based on the composition of each shopping basket. Design/methodology/approach This work uses scanner data to uncover product category interdependencies. As the number of possible relationships among them can be very large, the authors introduce an approach that generates an intuitive graphical representation of these interrelationships by usin

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Ammar, Ammar, and Yosua Setyawan Soekamto. "Pembuatan Sistem Rekap Penjualan Daster dan Mukena YashCollection di Marketplace untuk Pendukung Market Basket Analysis." Journal of Information System,Graphics, Hospitality and Technology 5, no. 1 (2023): 21–25. http://dx.doi.org/10.37823/insight.v5i1.237.

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Toko YashCollection merupakan salah satu perusahaan kecil dan menengah (UKM) yang bergerak di bidang penjualan daster dan mukena. Saat ini, toko YashCollection aktif berjualan di salah satu platform E-Commerce ternama yaitu Shopee. Semua proses pembuatan laporan, mulai dari pencatatan produk, pengecekan produk dan analisa produk yang akan dijual bersamaan masih dilakukan secara manual. Proses ini menyebabkan penentuan produk dan pengemasan produk menjadi lebih lama. Berdasarkan permasalahan tersebut, maka dilakukan penelitian “Pembuatan Sistem Rekap Penjualan Daster dan Mukena YashCollection d

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Starman, Terri Woods, Millie S. Williams, and James E. Faust. "Plant and Pinch Number for Alternative Hanging Basket Crops Affect Quality and Scheduling." HortScience 32, no. 3 (1997): 484E—484. http://dx.doi.org/10.21273/hortsci.32.3.484e.

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The objective was to determine the optimum number of plants and the number of pinches required to market a basket for hanging basket production using alternative floriculture species. The number of plants per pot varied from one to four, and the number of manual pinches per basket ranged from 0 to 2. Several species were evaluated in spring of 1996 and heat tolerance was assessed throughout the summer. Plugs (50–95 plugs per flat) were transplanted into 25-cm hanging baskets in a 22/18°C (venting/night temperature set points) glasshouse. Three to four plants were necessary for Scaevola aemula

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Susanto, Jonathan, and Indah Fenriana. "Web-Based POS with Apriori Market Basket Analysis." bit-Tech 6, no. 3 (2024): 271–80. http://dx.doi.org/10.32877/bt.v6i3.926.

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This research project focuses on the design and development of a web-based Point of Sale (POS) application that incorporates advanced analytical techniques, specifically Market Basket Analysis with the Apriori algorithm and the Association Rule method. The primary objective of this web-based POS system is to empower retailers in managing their sales transactions efficiently and gaining valuable insights into customer purchasing behavior. The web-based POS application is multifaceted, offering features such as sales transaction recording, product inventory management, and customer data tracking

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Malati, N., Ruchika ., and Bhawna Warikoo. "Market Basket Analysis and Product Affinity in Retail." Effulgence-A Management Journal 15, no. 1 (2017): 25. http://dx.doi.org/10.33601/effulgence.rdias/v15/i1/2017/25-38.

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Ho, Wai Tung, and Fu Wing Yu. "Chiller system performance management with market basket analysis." Facilities 39, no. 9/10 (2021): 667–87. http://dx.doi.org/10.1108/f-09-2020-0107.

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Purpose This study aims to apply association rule mining (ARM) to uncover specific associations between operating components of a chiller system and improve its coefficient of performance (COP), hence reducing the electricity use of buildings with central air conditioning. Design/methodology/approach First, 13 operating variables were identified, comprising measures of temperatures and flow rates of system components and their switching statuses. The variables were grouped into four bins before carrying out ARM. Strong rules were produced to associate the variables and switching statuses with

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Barman, Debaditya, and Nirmalya Chowdhury. "Movie Business Trend Prediction using Market Basket Analysis." International Journal of Computer Applications 74, no. 9 (2013): 38–46. http://dx.doi.org/10.5120/12916-9935.

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Szymkowiak, Marcin, Tomasz Klimanek, and Tomasz Józefowski. "APPLYING MARKET BASKET ANALYSIS TO OFFICIAL STATISTICAL DATA." ECONOMETRICS 22, no. 1 (2018): 39–57. http://dx.doi.org/10.15611/eada.2018.1.03.

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Hernawati, Hernawati, and Muhadi Hariyanto. "MARKET BASKET ANALYSIS TREN HIJAB MENGUNAKAN ALGORITMA APRIORI." INTI Nusa Mandiri 16, no. 2 (2022): 57–62. http://dx.doi.org/10.33480/inti.v16i2.2763.

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Diketahui perekonomian Indonesia sedang pemulihan akibat pandemi Covid 19 Toko, restoran, hingga pusat perbelanjaan yang biasa ramai pengunjung kini nampak sepi pengunjung akibat adanya anjuran untuk membatasi aktivitas di luar rumah. Bahkan tidak sedikit pula karyawan dari berbagai perusahaan terpaksa dirumahkan akibat pandemi. Covid 19 memang membawa dampak buruk bagi sektor industri, namun disisi lain, ini waktu yang tepat untuk membuka usaha secara mandiri dengan memanfaatkan teknologi. Untuk melakukan strategi pemasaran perlu adanya ilmu data mining menggunakan algoritma apriori untuk men

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RITU, KHANDELWAL, DIVYASHARMA, and KANWAR HARSH. "ANALYSING CUSTOMER'S PURCHASING PATTERN BY MARKET BASKET ANALYSIS." i-manager's Journal on Computer Science 7, no. 1 (2019): 43. http://dx.doi.org/10.26634/jcom.7.1.15582.

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45

Cavique, Luís. "A scalable algorithm for the market basket analysis." Journal of Retailing and Consumer Services 14, no. 6 (2007): 400–407. http://dx.doi.org/10.1016/j.jretconser.2007.02.003.

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Chen, Yen-Liang, Kwei Tang, Ren-Jie Shen, and Ya-Han Hu. "Market basket analysis in a multiple store environment." Decision Support Systems 40, no. 2 (2005): 339–54. http://dx.doi.org/10.1016/j.dss.2004.04.009.

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Yanti, Roaida, Jundi Nourfateha Elquthb, Ira Promasanti Rachmadewi, and Qurtubi Qurtubi. "Bibliometric study of association rule-market basket analysis." International Journal of Advances in Applied Sciences 13, no. 2 (2024): 282. http://dx.doi.org/10.11591/ijaas.v13.i2.pp282-290.

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Abstract:

Association rule-market basket analysis (AR-MBA) is a data mining technique for finding distinguished relationship patterns from a collection of items. The application of AR-MBA is also increasingly widespread, starting from retail and hotels to hospitals. So, bibliometrics related to AR-MBA needs to be done to reveal what research opportunities can be later carried out by reviewing and analyzing publications about AR-MBA. 91 bibliographies in 1 decade from 2012-2022 were collected using Harzing's Publish or Perish (PoP). VOSviewer is also employed to map authorship and publication topic trend

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48

Purwati, Neni, and Sri Karnila. "Strategi Peningkatan Penjualan Produk Menggunakan Market Basket Analysis." Jurnal Sistem Informasi Bisnis 13, no. 2 (2023): 96–103. http://dx.doi.org/10.21456/vol13iss2pp96-103.

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Toko Galo-galo merupakan toko yang menjual hasil produk UMKM berupa makanan, yang awalnya hanya menjual produk somay, dengan semakin meningkatnya permintaan pelanggan, maka toko menambah aneka makanan frozen food seperti bakso, sosis, naget dan lain-lain. Melihat kebutuhan pelanggan yang beraneka ragam, maka salah satu strategi yang dilakukan yaitu menganalisis aneka produk apa saja yang dibeli secara bersamaan, sehingga dapat menjadi rekomendasi bagi toko dan bagian pemasaran mengenai kombinasi produk yang diminati pelanggan. Proses pemasaran dan dokumentasi penjualan sebelumnya dengan menggu

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49

Verda, Damiano, and Marco Muselli. "Alternative Support Threshold Computation for Market Basket Analysis." AppliedMath 5, no. 2 (2025): 71. https://doi.org/10.3390/appliedmath5020071.

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This article aims to limit the rule explosion problem affecting market basket analysis (MBA) algorithms. More specifically, it is shown how, if the minimum support threshold is not specified explicitly, but in terms of the number of items to consider, it is possible to compute an upper bound for the number of generated association rules. Moreover, if the results of previous analyses (with different thresholds) are available, this information can also be taken into account, hence refining the upper bound and also being able to compute lower bounds. The support determination technique is impleme

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50

Roaida, Yanti, Nourfateha Elquthb Jundi, Promasanti Rachmadewi Ira, and Qurtubi. "Bibliometric study of association rule-market basket analysis." International Journal of Advances in Applied Sciences (IJAAS) 13, no. 2 (2024): 282–90. https://doi.org/10.11591/ijaas.v13.i2.pp282-290.

Full text

Abstract:

Association rule-market basket analysis (AR-MBA) is a data mining technique for finding distinguished relationship patterns from a collection of items. The application of AR-MBA is also increasingly widespread, starting from retail and hotels to hospitals. So, bibliometrics related to AR-MBA needs to be done to reveal what research opportunities can be later carried out by reviewing and analyzing publications about AR-MBA. 91 bibliographies in 1 decade from 2012-2022 were collected using Harzing's Publish or Perish (PoP). VOSviewer is also employed to map authorship and publication topic trend

APA, Harvard, Vancouver, ISO, and other styles

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