Market Basket Analysis Research Articles

Development of Ergo-Bundling System for Food Retailer based on Data Mining

In retail, a major challenge is quickly selling perishable food items like cakes or pastries within a limited time. If unsold, these products lead to losses, even with discount promotions. Retailers must forecast stock accurately and develop more effective strategies to meet consumer demand within the required timeframe. One suggested strategy is bundling, where high-demand products are combined with less popular ones. However, when done manually, this approach often fails to align with consumer preferences. This study aims to develop an automated bundling system using data mining techniques. Market Basket Analysis is used to understand consumer purchasing patterns, while Association Rules with the Apriori Algorithm help identify relationships between different products. These methods reveal which items are frequently bought together, making bundling strategies more effective. The system will be designed with usability and ergonomic principles, ensuring it is user-friendly. The implications of this system include improved stock management, more accurate bundling, and better alignment with customer preferences, ultimately increasing satisfaction. Additionally, automation reduces errors and inconsistencies that occur with manual bundling. The expected outcome is a more efficient, effective, and comfortable system for food retailers, leading to higher sales, reduced losses, and greater overall customer satisfaction.

IPHM: Incremental periodic high-utility mining algorithm in dynamic and evolving data environments

Periodic high-utility itemset (PHUI) mining can extend beyond the conventional approach of high-utility itemset mining by uncovering recurring customer purchase behaviors common in real-life scenarios (e.g., buying apples and oranges every three days or weekly). Such behaviors, particularly in market basket databases, signify stable patterns that ensure long-term profitability. Existing PHUI mining algorithms assume a static database and incur significant costs when handling incremental databases, as each batch of new transactions necessitates reprocessing the entire dataset. To overcome this challenge, we introduce the Incremental Periodic High-Utility Itemset Miner (IPHM), a method for efficiently extracting periodic high-utility itemsets in incremental database environments. We propose an innovative incremental utility-list structure tailored for incremental database scenarios. Effective pruning strategies are employed to expedite the construction and update of incremental utility-lists and to discard unpromising candidates. As demonstrated by the experimental results, the algorithm is efficacious and efficient, highlighting its practical applicability in dynamic data environments. The algorithm shows a remarkable ability to quickly adapt to database changes, making it highly suitable for applications in market basket analysis where frequent updates are common.

The Layout Planning At Sehati Dua Minimarket, Ciawigebang, Kuningan

The layout is one of the factors that affect visitor satisfaction. Where a good design will be the face of the company itself. There is a layout problem in the Sehati minimarket location where the product arrangement displayed has not been neatly arranged. The display of goods is placed directly on the floor without using any shelves. One way to optimize the relationship between product group proximity and use of space or layout. This research uses Market Basket Analysis and Activity Relationship Chart method by measuring three kinds of quantitative measures, namely support (factor domination level), confidence (factor level) and the ratio of improvement (viability factor proximity) and relationship product proximity qualitatively. Results of data processing and analysis with the method of MBA and Tolls ARC approach there are several alternatives to the new layout improvement then selected on of the best choice by the management company with a value of 40, and some changes from the previous layout the product – the product that must be moved among product with value the smallest, and development with an enormous support value,(1) namely The Gelas products

Penerapan Strategi E-Commerce Untuk Meningkatkan Penjualan Pada Butik Rasyaf Collection Dengan Metode Market Basket Analysis

Rasyaf Collection Clothing Store offers a wide variety of women's clothing, including dresses, pants, accessories, shoes, jackets, and more. Currently, Rasyaf Collection Boutique's marketing system relies on social media platforms like Facebook, WhatsApp, and Instagram. However, the boutique owner has not yet implemented effective marketing strategies, making it difficult to reach customers, as they have to visit the boutique in person to purchase clothing, which limits the marketing reach. One of the challenges hindering the development of this business is the lack of product information available to potential customers, such as difficulties in identifying the types of products being sold and managing product inventory. The development of this system implements a prototyping model and is carried out in stages or linearly. It involves analyzing customer behavior from specific groups using market analysis methods. When specific patterns are unknown, market basket analysis is typically used as a starting point to gather information about transaction data. Data sources for market basket analysis include credit card transactions, discount coupons, lottery tickets, and customer complaint calls. The results of this research indicate that the developed e-commerce website application can assist the boutique owner in selling various types of clothing and managing product inventory. Additionally, this application will make it easier for customers to access information about the products being sold and allow them to choose clothing from Rasyaf Collection Boutique.

Smart Product Recommendations in Web E-Commerce: Leveraging Apriori Algorithm for Market Basket Analysis

Smart Product Recommendations in Web E-Commerce: Leveraging Apriori Algorithm for Market Basket Analysis

Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Pada Toko Dimsum Madani Menggunakan Algoritma Apriori

Data mining is a technique for extracting new information from data warehouses, information is seen as very important and valuable because by mastering information it is easy to achieve a goal, this makes everyone compete to obtain information, as is the case with the Dimsum business at Dimsum Madani.toko. This is located on Jalan Lampu gg. Pelita 4, Brayan Bengkel, East Medan, the location is close to many Brayan Resident's Houses. This of course affects sales levels. Increasing daily sales activity results in an accumulation of sales transaction data that continues to increase, thereby burdening data storage. Unfortunately, this data is only stored without further processing. In fact, this data collection holds valuable information.This research uses Market Basket Analysis with the Apriori Algorithm to find association patterns based on consumer shopping behavior. The goal is to identify items that are often purchased together. The research results showed that the combination of Seaweed Dimsum with Tofu Skin Spring Rolls had the highest support value (50%) and the highest confidence (75%).

Penerapan Algoritma Apriori Dalam Analisis Keranjang Belanja Retail Di Wilayah Jawa Barat

Market basket analysis is an important technique in data mining used to understand consumer purchasing patterns. This research uses the Apriori algorithm to identify relationships between products in the shopping basket, aiming to improve sales and marketing strategies in the retail industry. The focus of this study is on retail transaction data from West Java Province, which has a large and diverse population, reflecting complex consumer purchasing patterns. The research identifies several key issues: limited understanding of consumer behavior, unoptimized business strategy opportunities, and challenges in managing large transaction data. As a solution, the application of the Apriori algorithm can help find frequent consumer purchasing patterns and design more effective marketing strategies. The results show that market basket analysis using the Apriori algorithm is effective in understanding consumer purchasing patterns in the retail industry. This algorithm allows companies to discover itemsets that frequently appear together in transactions, which can be used to design more effective marketing and sales strategies.

Analysis of consumer characteristics on retail business with clustering analysis method and association rule for selling improvement strategy recommendations

In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.

Frequent Pattern Associative Rules in Multiple Dimensions

This paper explores frequent pattern mining in multidimensional data and its extension beyond traditional one-dimensional approaches. While association rule mining is widely used in market basket analysis, its application to multidimensional datasets presents new challenges due to the increased complexity and sparsity of the data. This research reviews existing multidimensional mining techniques, focusing on frequent pattern associative rules, and evaluates their performance. Several real-world applications, such as business intelligence, healthcare analytics, and sensor networks, are discussed. The paper also proposes a framework for efficient pattern discovery and suggests areas for future research

Implementasi Algoritma Apriori pada Market Basket Analysis terhadap Data Penjualan Produk Supermarket

Market Basket Analysis (MBA) is an analytical technique used to identify relationships between items in purchasing transactions. This notebook uses retail transaction datasets and the Apriori algorithm to discover hidden associations and patterns that retailers can leverage in optimizing marketing strategies, store layouts, and product recommendations. Through initial data processing, data exploration, and application of the Apriori algorithm, this analysis succeeded in identifying various significant associations between items that are frequently purchased together. The results provide valuable insights for retailers to develop targeted promotions and improve customer shopping experiences, while emphasizing the importance of selecting the right parameters to obtain accurate and relevant results.

Towards episode rules with non-overlapping frequency and targeted mining

Data mining has become a popular task due to the explosion of rich data. Frequent episode mining (FEM) is an effective technique for extracting valuable and crucial information from event sequences, playing a significant role in various fields such as market basket analysis, association analysis, and management sciences. A variety of FEM algorithms discover frequent episode rules using the frequency function and anti-monotony. Non-overlapping frequency works well for mining episode rules because it cuts down on unnecessary computations and still follows anti-monotonicity. Leveraging this technique, we propose the NONEPI+ algorithm for discovering rules that were previously overlooked. However, there are no algorithms that can be used to discover episode rules that contain target query rules. To fill the research gap in episode rule mining algorithms, we further propose a novel algorithm called TaER for targeted mining of episode rules, i.e., a set of rules containing specific query rules. TaER can successfully discover the episode rules that the users are more specifically interested in. It can provide direction for prediction tasks in many aspects, such as weather observation, network intrusion, e-commerce, and financial behavior prediction. All source code and datasets are available on GitHub https://github.com/DSI-Lab1/TaER.

Apriori Algorithm and Market Basket Analysis to Uncover Consumer Buying Patterns: Case of a Kenyan Supermarket

This article presents a study on utilizing the Apriori algorithm and Market Basket Analysis (MBA) to reveal consumer buying patterns in supermarkets. The aim of this research is to explore the effectiveness of these data mining techniques in revealing valuable insights that can inform marketing strategies and enhance the overall shopping experience for customers. This study centered on improving customer loyalty within the supermarket setting through the utilization of cutting-edge information technology and programming applications, including Python. Specifically, the Apriori algorithm libraries of the Python language were employed to identify frequent item sets and derive 42 association rules, which shed light on product affinities and co-purchasing patterns. By deriving association rules from the frequent item sets, the study identified the significance of strategically placing frequently purchased products to enhance revenue generation. In conclusion, the application of the Apriori algorithm and Market Basket Analysis in this case of a Kenyan supermarket has proven to be a valuable approach for uncovering consumer buying patterns, providing a competitive edge in the dynamic retail industry.

Market Basket Analysis

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 important aspect of a retail organization's analytical framework for deciding where products should be placed and developing sales promotions for various segments of consumers to increase customer loyalty and, as a result, benefit. Market Basket Analysis is a data mining technique that can be used in various fields, such as marketing and etc. The frequent itemsets are mined from the database using the Apriori algorithm and then the association rules are generated. The project will assist supermarket managers in determining the relationship between the items that their customers purchase.

From Data to Decisions: Optimizing Supply Chain Management with Machine Learning-Infused Dashboards

This paper examines how business users can leverage machine learning and data analytics through dashboards to optimize their decision making in demand-side supply chain management. We present a case study of an Austrian B2B hygiene product retailer that needed to provide its top management, sales representatives, and marketing managers with more relevant information to improve business intelligence and to enhance customer acquisition and retention. To generate this information, we utilized various data analysis and machine learning methods, including RFM analysis, market basket analysis, TURF analysis, and demand forecasting, using real-life transaction data. To provide business users with easy access to this information, we developed dashboards that integrate these methods providing an interactive and visual tool for data exploration and understanding. We conclude that dashboards can enable, business users to make better informed and effective decisions on the demand side of supply chains leading to improved sales performance and increased customer satisfaction.

Enhancing Retail Supermarket Financial Performance Through Market Basket Analytics Using Apriori Algorithm in Indonesia Market Case

Market Basket Analysis is a powerful technique in data mining and retail analytics that explores associations and patterns among items frequently purchased together by consumers. This technique reveals insights into consumer purchasing behaviour and facilitates the creation of compelling product bundles. This study identifies five distinct product bundling strategies tailored to diverse consumer personas prevalent in the Indonesian market, such as "Health enthusiast", "Exotic Flavor Explorer", "Food Enthusiast", "Fitness Freak", and "Budget-conscious Home Cook". These product bundling strategies leverage market basket analysis to enhance the shopping experience, meeting Indonesian consumers' diverse preferences and lifestyles in the retail supermarket landscape. The analysis provides a basis for effective promotional campaigns and personalized marketing efforts. By recognizing associations between products, supermarkets in Indonesia can design targeted promotions, encouraging customers to explore complementary items and potentially increase their overall spending.

-COMMERCE DATA ANALYSIS

In an increasingly digital and data-driven business landscape, e-commerce has become a critical domain for companies seeking to understand customer behaviour, enhance operational efficiency, and boost profitability. This project embarks on an exploration of e-commerce data analysis, focusing on its application in business optimization. In today's digital landscape, e-commerce platforms have revolutionized the way businesses operate and consumers shop. With the exponential growth of online transactions, the need to understand consumer behaviour and optimize business strategies has become paramount. This project aims to delve into the realm of e-commerce data analysis, leveraging diverse datasets to extract meaningful insights and drive informed decision-making. The study commences with data collection from multiple sources, including user interactions, purchase histories, demographics, and website traffic. Various analytical techniques such as descriptive analytics, predictive modelling, and machine learning algorithms are employed to extract valuable patterns, trends, and correlations from the amassed data. The findings from this analysis are crucial for ecommerce businesses to streamline operations, enhance customer satisfaction, and drive revenue growth. The project aims to contribute actionable insights that can be utilized by e-commerce stakeholders to make data driven decisions and stay ahead in a highly competitive market. The objectives of this study are to investigate and synthesize the existing literature on various facets of e-commerce data analysis, encompassing frameworks, data sources, metrics, customer behaviour analysis, personalization, recommendation systems, fraud detection, security measures, market basket analysis, cross-selling strategies, supply chain management, inventory control, user experience enhancement, A/B testing, and ethical considerations. By assessing these key areas, this project seeks to provide a comprehensive understanding of how data analysis techniques can be harnessed to improve e-commerce businesses' decision-making, customer satisfaction, and security. With the exponential growth of e-commerce platforms in recent years, there is a wealth of data available that offers valuable insights into consumer behaviour, market trends, and business performance. This study aims to conduct a thorough analysis of e-commerce data to uncover meaningful patterns and trends, providing businesses with actionable intelligence to enhance decision-making processes. The research methodology combines descriptive statistics, machine learning

A Novel Approach to Enhance the Efficiency of Apriori Algorithm

Data mining is the process of obtaining valuable or significant information from a large-scale database. One significantarea of research in the field of data mining is association rules mining. Apriori algorithm is one of the classical algorithmsin the association rule mining field. This research analyses the basic ideas and shortcomings of the Apriori algorithm andcompares several different styles of its major improvement strategies. Then it suggests an improved version of the Apriorialgorithm that utilizes a dataset summarization method, an optimized database mapping technique, an intersection operation,and a joined optimization strategy. These enhancements aim to address the low performance and efficiency by reducing thegeneration of candidate itemsets and minimizing the execution time. This addresses the issues of generating numerous candidate itemsets and repeatedly scanning the transaction database. After implementing the optimized algorithm, to verify its effectiveness, it has been applied to a groceries dataset, which is for market basket analysis. The improved Apriori algorithm demonstrated significant enhancements over the original algorithm in terms of reduced candidate itemsets and running time, leading to improved algorithm efficiency.

Reconstructing thicket clump formation using association rules analysis

AbstractAimVegetation change such as woody encroachment is characterised by changing species interactions, and processes such as competition and facilitation may be inferred from patterns of association between juveniles and mature individuals of different species. Our aim was to apply and evaluate association rules analysis (ARA), a rule‐based data‐mining technique more commonly known as market basket analysis, as a novel tool to examine the associations between woody species in different demographic stages along a thicket encroachment gradient.LocationThe research was conducted in a subtropical thicket–savanna mosaic (730 mm mean annual rainfall) in the Eastern Cape, South Africa.MethodsWe used a space‐for‐time substitution approach and sampled woody plants in different size classes at sites representing early, intermediate and late stages of encroachment. All individuals were recorded as occurring singly or in unique clumps. We used ARA to determine which associations between species in different size classes were common overall, and more common than expected, at each of the three encroachment stages.ResultsThe most important association rules indicated that Vachellia karroo recruited singly and in large numbers in open grassland and, once mature, provided nucleation sites for a small suite of species dominated by Scutia myrtina. In the later stages, multiple diverse associations were found in increasingly large clumps.ConclusionsOur sampling approach and ARA proved useful for characterising common species‐size class associations, illuminating changing species interactions and recruitment patterns along a thicket clump formation sequence. In studies of vegetation change, ARA can complement multivariate analyses of species composition to reveal specific associations, and it can provide a less laborious alternative to point‐pattern analysis for elucidating spatial associations.

Web-Based POS with Apriori Market Basket Analysis

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. What sets it apart is the integration of the Apriori algorithm and Association Rule method, which enable the system to analyze and understand customer purchasing patterns. It identifies strong product associations and establishes rules that support intelligent decision-making for businesses. The advantages of Market Basket Analysis are substantial. Retailers can identify relevant purchase patterns, such as frequently co-purchased products or cross-selling opportunities. This information can be used to enhance marketing strategies, optimize product placement in stores, and create bundled product offerings, ultimately boosting sales and revenue. By analyzing transaction data and recognizing patterns, retailers can streamline their operations, minimize wastage, and allocate resources more effectively. In summary, this research project showcases the transformative potential of integrating Market Basket Analysis, the Apriori algorithm, and the Association Rule method into web-based POS systems. By doing so, retailers can enhance operational efficiency, boost sales, and improve customer satisfaction, ultimately leading to more successful and competitive businesses in the retail sector.

Designing Animal Market Layout by Considering Consumer Purchase Behaviors

Sales transaction data contains rich information and can support company competitiveness. However, this transaction data is initially unstructured and needs to be processed into insight for the company's decision-making. Market Basket Analysis (MBA) is a data mining technique that can be used to study consumer purchasing patterns. This paper presents a case study on using an MBA to obtain consumer buying behaviors where the result of the MBA is then used to design a proposed layout. The animal market governed by Yogyakarta Province, known as Pasty Market, was used as a case study. Pasty Market is an animal trading center with around 30,000 square meters area and 255 sellers that sell various kinds of animals such as songbirds, dove birds, rabbits, cats, dogs, iguanas, turtles, ornamental chickens, and ornamental fish as well as animal food and cage. With this enormous area and number of merchants, the layout of Pasty Market becomes crucial in customer satisfaction. Association rules result in four priority levels in proposed layout planning, where these rules are used to determine the proximity among items in the proposed layout. These four levels of priority, ordered by the confidence value, are (1) “songbirds” and “bird food” (confidence value 91%), (2) “ornamental fish” and “turtles” (confidence value 80-90%), (3) “birdcages” and “songbirds” (confidence value 70-80%), and (4) “cats” and “dog” as well as “birdcages” and “birds” (confidence value 50-70%). Association rules were then used as the basis for determining the proximity value between merchants, where the proximity rules were then used for designing a proposed layout.