Market Basket Research Articles

Analisis Pengambilan Keputusan Untuk Korelasi Pembelian Produk Menggunakan Metode Association Rules

Business people are required to be able to keep up with the increasingly dynamic times. Business people must have the right strategy to be able to compete with competitors so that the business they run continues to generate profits. From the existing sales transaction data, it can be used for decision making for business people in developing a good strategy in developing their business. By using data mining with the Association Rules method can be used to analyze existing sales patterns. In this test, the author uses the Apriori Algorithm to analyze the market basket analysis to find out the correlation between product purchases and products that are often purchased by consumers. From the analysis of the a priori algorithm, it produces frequent items that meet the minimum values ??of support and confidence. The result of this test is to provide product recommendations to consumers based on interrelated items and as a reference in product promotion to support decisions in developing good strategies in their business.

Market Basket Analysis of a Health Food Store in Thailand

This article presents a market basket analysis of a health food store in Thailand. The analysis identifies data attributes that frequently occur together in the dataset. Frequent occurrences of data attributes representing customer purchasing behaviors are extracted as association rules using the frequent pattern growth algorithm. The generated associations are evaluated using standard measures based on occurrence counts and an additional financial measure. Marketing strategies in the form of cross-selling pairs of specific products are then designed based on the data attributes appearing in the significant associations. The cross-selling products are offered at discounted prices and promoted in marketing campaigns. A break-even analysis is performed to estimate the required number of additional sales volumes from each marketing campaign to compensate for the discounted prices. The presented use case demonstrates the effectiveness of extending the market basket analysis to include a financial measure that can lead to practical marketing campaigns.

Optimization of payroll funds in construction industry

This study analyzes a new method for determining the minimum subsistence level based on the median wage in regions with the purpose of exploring the impact of this method on payroll funding in construction industry. The research methodology involved economic and statistical analysis, system analysis, as well as integration of theoretical and practical knowledge. The authors analyzed payroll fund as part of the cost estimate documentation and the wages stipulated in the “Federal Industrial Agreement on the Construction and Building Materials Industry of the Russian Federation, 2020-2023”. The authors analyzed the new method proposed by the Russian Government for determining the minimum subsistence level on the basis of the median wage, explored its advantages and disadvantages, and introduced ideas for its optimization. According to the results obtained, the changes introduced in the method for calculating the minimum subsistence level in 2021 failed to produce the expected results. The method needs to be improved in the following part. The official indicators of regional median wages should be calculated at least quarterly and posted on the official website of Rosstat. The method should justify the percentage (44.2%) adopted for the calculation of the minimum subsistence level based on the median wage. In addition, a low threshold should be established for the minimum subsistence level in a region equal to the cost of the market basket. In order to be calculated accurately, the cost of the market basket should be adjustable in terms of the content of all its sections.

Enhancing Retail Strategies through Apriori, ECLAT& FP Growth Algorithms in Market Basket Analysis

"Market basket analysis" is a method employed in data mining to discover items that are commonly bought together by customers in a retail store. It is a crucial tool for retailers to understand consumer purchasing behavior and to improve their sales and marketing strategies. In this research paper, we present a comprehensive study on market basket analysis using three popular algorithms: Apriori, ECLAT, and FPGrowth. The paper begins with a brief synopsis of market basket analysis and the techniques adopted for itemset mining. We then introduce the dataset used in this study, which consists of real-life transaction data collected from a retail store. Next, we perform a thorough evaluation of the Apriori, ECLAT, and FPGrowth algorithms in terms of their computational time and the quality of the association rules generated. The results show that the FPGrowth algorithm is the fastest of the three algorithms, while the Apriori algorithm generates the most comprehensive and high-quality association rules.In addition, we also present a comparison of the performance of these algorithms that involve different assessment criteria like support, confidence, and lift. Our study highlights the importance of selecting the appropriate algorithm for market basket analysis depending on the specific requirements and constraints of the task. The paper concludes with an analysis on the limitations and future directions of research in this area. Overall, our study provides insights into the strengths and weaknesses of the Apriori, ECLAT, and FPGrowth algorithms and functions as a valuable resource for professionals and researchers in the field of market basket analysis.

Clustering of Non-Associated Item Sets for Analyzing Show Room Sales Dataset

Market basket analysis (MBA) is a well-liked method for identifying relationships between products that people purchase in a database. It is predicated on association rule mining (ARM), a data mining technique that pulls valuable data from huge databases. Due to consumers using internet applications for online shopping and insurance, an ever-increasing amount of data is generated online. It produces large amounts and, if mined effectively, will greatly benefit society as a whole as well as individuals. So, numerous data science and machine learning-related techniques have been created to gradually unlock the potential. The Clustering of Non-Associated Item Sets (CNAIS) of the Sales dataset used in the Showroom for choosing customers for benefits and web application design is discussed in this study. The CNAIS algorithm implementation process and dataset for this study are discussed.

Enhancing Olive Phenology Prediction: Leveraging Market Basket Analysis and Weighted Metrics for Optimal Feature Group Selection

Similarly efficient feature groups occur in prediction procedures such as Olive phenology forecasting. This study proposes a procedure that can be used to extract the most representative feature grouping from Market Basket Analysis-derived methodologies and other techniques. The computed association patterns in this process are visualized through graph analytic tools, comparing centrality metrics and spacial distribution approaches. Finally, highlighted feature formations are located and analyzed within the efficiency distribution of all proposed feature combinations for validation purposes.

E-Commerce Market Basket Analysis using Apriori Algorithm

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 Behaviour, Data Mining

Discovery and occurrence of organophosphorothioate esters in food contact plastics and foodstuffs from South China: Dietary intake assessment

A recent study revealed the presence of non-pesticide organothiophosphate esters (OTPEs) — precursors to organophosphate esters (OPEs) contaminants — in river water. Since OPEs have demonstrated adverse reproductive outcomes in humans, this accentuates the urgency to explore the prevalence of non-pesticide OTPEs in other potential human exposure matrices. In this study, a nontarget screening method based on high-resolution mass spectrometry was used to identify OTPEs in food contact plastic (FCP) samples collected from South China. O,O,O-triphenyl phosphorothioate (TPhPt) and O,O,O-tris(2,4-di-tert-butylphenyl) phosphorothioate (AO168 = S) were unequivocally identified (Level 1), while O,O-di(di-butylphenyl) O-methyl phosphorothioate (BDBPMPt) was tentatively identified (Level 2b, indicating probable structure based on diagnostic evidence). Among n = 70 FCP samples, AO168 = S emerged with the highest detection frequency and median concentration of 74 % and 111 ng/g, respectively. Significant Pearson correlations were observed in log-transformed peak areas of AO168 = S and TPhPt in FCPs with their respective oxons, respectively. Occurrences of AO168 = S and TPhPt were further investigated in n = 100 foodstuff samples using a market basket method. AO168 = S and TPhPt exhibited detection frequencies of 43 % and 44 % in all food items with mean concentrations of 2.17 ng/g wet weight (ww) (range: <0.53–67.8 ng/g ww) and 0.112 ng/g ww (range: <0.006–2.39 ng/g ww), respectively. The highest mean concentrations for AO168 = S and TPhPt were found in vegetables (4.62 ng/g ww) and oil (3.00 ng/g ww), respectively. The median estimated daily intakes (EDIs) of AO168 = S and TPhPt via diet were calculated as 10.4 and 1.51 ng/kg body weight/day, respectively. For AO168 = S, only meat and vegetables contributed to the median EDI, whereas for TPhPt, oil was identified as the principal contributor to the median EDI. This study for the first time evaluated human exposure to OTPEs via diet, providing new insights to overall human exposure to OPEs.

Analysis of the Application of Transaction Data with Association Techniques using the Apriori Algorithm in Pharmacy

The development of information technology influences the rapid growth in the amount of data collected and stored in large t. Dimas Pharmacy, located at Jl. Segara Kec. Nipah Panjang is one of the public health services that sells various medicines, medical devices, and so on. This study is expected to provide positive benefits for owners of Dimas Nipah Panjang Pharmacy in Providing information about the pattern of medicine purchases made by consumers and Facilitating pharmacy owners to know the available medicine supplies in the warehouse so as not to experience emptiness when needed. Problem Formulation, Literature Study, Data Collection, Calculation and Analysis of Associations with Priori Algorithms, Results Evaluation and Analysis and Report Making. Based on the results of interviews and observations that have been made, the authors obtain data from the Dimas Pharmacy sales transaction. Data held ± 1000 sales transaction data for the period of May and June. But the author only entered 216 sales transactions in May and 141 sales transactions in June. After knowing the method of data selection, the authors conducted data selection by taking 6 items of significant data specifications in certain contexts, namely Anti Serotonin / Allergy, Antacid / Ulcer, Antibiotics, Antipyretics, Inflammation, Hypertension Each of these items had different brands. From these results it can be explained that the sales transaction of the Dimas Pharmacy in May and June generates or generates relationships between shopping product items. With the calculation of the Apriori Association Algorithm, a Market Basket Analysis relationship was found between Medicinal Pronicy and Dexa items. With the Rule "IF Buy Pronicy, THEN Buy Dexa". The rule is generated from the highest support and confident values of the overall support and confident items. The highest support value is 0.15 and the highest confident value is 0.5 ".

High-resolution mass spectrometry-based screening and dietary intake assessment of organophosphate esters in foodstuffs from South China

Organophosphate esters (OPEs) are a group of emerging contaminants with widespread environmental occurrence, yet research on their occurrence in foodstuffs is limited. We collected 100 foodstuff samples in South China using a market basket method, and analyzed food extracts for the presence of OPEs and organophosphite antioxidants (OPAs) by suspect and nontarget screening through high-resolution mass spectrometry. Our analysis resulted in the identification of 30 OPEs, comprised of 25 OPEs with a confidence level (CL) of 1 (unequivocal identification using standards) and five OPEs with CL = 2b (probable structure based on diagnostic evidence). Interestingly, 11 of these identified OPEs had not been previously reported in food. No OPA was identified. The occurrence of identified OPEs within the food samples was further investigated. The highest median concentration of OPEs in all food samples was reached by tris(2-chloroisopropyl) phosphate (TCPP) (1.55 ng/g ww, range < 0.74–12.0 ng/g wet weight (ww)). Cereals demonstrated the highest median concentration of the cumulative 30 OPEs. Tris(2-chloroethyl) phosphate (TCEP), TCPP, and triethyl phosphate (TEP) predominantly contributed to OPEs contamination in most food categories. Eight OPEs, namely TEP, tris(2-ethylhexyl) phosphate (TEHP), TCEP, triphenyl phosphate (TPhP), 2-ethylhexyl diphenyl phosphate (EHDPP), bis(2-ethylhexyl) phenyl phosphate (BEHPP), resorcinol bis(diphenyl phosphate) (RDP), and methyl diphenyl phosphate (MDPP) exhibited significantly higher concentrations in the processed group as compared to non-processed group, suggesting that food processing may result in contamination of these OPEs. The median sum of estimated dietary intake (ΣEDI) of all OPEs was determined to be 161 ng/kg body weight/day. Cereals (38.5 %) and vegetables (23.5 %) were the predominant food categories contributing to ΣEDI, and TEP (29.0 %), TCEP (20.2 %), and TCPP (18.3 %) were three major OPEs contributing to ΣEDI. This study for the first time offered a comprehensive overview of OPE species and revealed their occurrence in foodstuffs from South China.

Unveiling the Wave: Unraveling Sales Trends and Unlocking Market Basket Insights

This article presents sales trends and market basket analysis for Food and Beverages (F&amp;B) items recommendation using linear regression time series and association rules. The purpose of this study is to help businesses to make better informed decisions by understanding their customers’ purchasing behaviors and the sales trends of the F&amp;B items. This includes the study of both cross- selling and up-selling patterns using market basket analysis. To improve the market basket analysis, food name clustering has been carried out to group those foods having similarity ratio higher or equal to 80 based on the Levenshtein distances between the food names. This study analyzes sales data from a mobile app platform that register the Malaysian F&amp;B hawkers (or merchants) and app users (or buyers) who sell and purchase F&amp;B respectively. This study is based on sales trends analysis and association rules of product recommendations by examining FP-Growth and the relationship between product items bought. This study provides valuable insights for local Malaysian hawkers who are looking to improve their F&amp;B items recommendations and to make better data-driven decisions while planning their marketing activities. It helps to improve sales by either up or cross-sell their products more effectively.

ANALISIS DATA PENJUALAN PADA SUPERMARKET XYZ MENGGUNAKAN METODE MARKET BASKET

The economic growth in Indonesia has increased significantly, positioning Indonesia as the 4th largest economy after China, India, and Malaysia. This is also accompanied by national retail sales reaching a total of Rp6,044 trillion. One example of a retail business in Indonesia is Supermarket XYZ, which operates in Indonesia and sells essential items such as sugar, eggs, butter, and others. This research discusses an issue that exists in Indonesia, for example, Supermarket XYZ. The supermarket faces a problem with its employees struggling to determine the appropriate product layout based on consumer purchasing behavior. The market basket analysis method is used in this research to assist Supermarket XYZ's employees in determining the product layout. Transaction data is collected through internet searches, specifically on the Kaggle website. The data is then processed using the market basket analysis method, both manually and with the aid of the RapidMiner tool, to ensure the validity of the calculations. Once obtained, the results are implemented into a program to provide predictions for product placement layout. The research findings show that out of the 10 most purchased products by consumers, the top four products are chocolate, apples, bread, and ice cream. Furthermore, the suitable product combinations for layout placement yield 3 combinations: chocolate placed near apples with a support rate of 23%, chocolate placed near bread with a support rate of 22%, and chocolate placed near ice cream with a support rate of 21%.

Evaluating the Performance of Association Rules in Apriori and FP-Growth Algorithms: Market Basket Analysis to Discover Rules of Item Combinations

This study focuses on applying data mining techniques, especially association rules mining using the Apriori and FP-GROWTH algorithms, for market basket analysis on PT. XYZ is a pharmaceutical company in Indonesia. A quantitative methodology uses a dataset of 100,498 transactions originating from 432,356 rows of data covering July to December 2022 in the JABODETABEK area. Apriori and FP-GROWTH algorithms are applied for association rules mining. The results show that FP-GROWTH has the fastest execution time of 84,655 seconds. However, the memory usage for the Apriori algorithm is the lowest at 482.32 MiB, with increments of: 0.21 MiB. For the rules generated, the two algorithms, both Apriori and FP-GROWTH, produce the same number of rules and values of support, confidence, lift, Bi-Support, Bi-Confidence, and Bi-Lift. In conclusion, Apriori is recommended for sales datasets if memory usage and ease of implementation are important. However, if the speed of execution time and a large amount of data are considered, FP-GROWTH is a better choice because the execution time is faster for large amounts of data. However, the choice of algorithm depends on the specific analysis objectives, itemset size, data scale, and computational capabilities. Results from association rules mining provide evidence of product popularity, purchasing patterns, and opportunities for strategic marketing and inventory management. These findings can help PT. XYZ improves business efficiency, understands customer behavior, and increases profitability.

Customer Spending Pattern Analysis with Market Basket Analysis to Produce Product Strategy and Price Bundling at Kopi Soe Mekarwangi Bandung

Coffee industry in Indonesia will always grow significantly. With the return of consumptions in coffee shops and take away coffee shops after the pandemic, Kopi Soe Mekarwangi Bandung as one of the players in Bandung’s coffee industries hope to experience increases in sales and profit. But what happened was the contrary, for in January 2022-January 2023, Kopi Soe Mekarwangi experienced a decline in their sales and profit. Based on the data gained from the International Coffee Organization and Toffin, coffee shops should be increasing their sales in that period. To get a deeper knowledge about the research object, an interview with the owner of Kopi Soe Mekarwangi Bandung was done and it was found that they have one fundamental marketing problem in the term of their bundle. For their bundling strategy, the basis of their decision is either (1) introducing new products, or (2) boosting slow moving products. Based on the literature and journals, it was found that the best basis for marketing strategies should be the customer spending pattern. The customer’s spending pattern can be seen in their transactions and can be analyzed using data mining techniques such as market basket analysis. It’s hoped that the result of this mining can be used to determine the best bundling strategies for Kopi Soe Mekarwangi Bandung in the future.

Determining the feasibility of using the automated market basket analysis method to investigate a cause-and-effect pattern of construction accidents and its safety associations

Construction sites are complex and dangerous. Over the past decade, construction fatality rates have been high in most countries. Construction accidents cause harm to construction workers and financial loss to construction firms. Smart wearable jackets are among the most effective personal protection equipment (PPE) for India's most recent and future generations. Prevention is better than Cure. To prevent the occurrences of construction accidents and to provide better safety and health to construction workers, the sensor data has to be collected from the IoT environment and has to make it subjected to cloud-based big data analytics to provide better decision support to project managers and doctors. Further, the decision support system can be enhanced by adding semantic capabilities using Ontology, Semantic Web Services, and data mining and artificial intelligence techniques. This study highlights the feasibility of using the automated market basket analysis method to investigate the cause-and-effect pattern of construction accidents, especially when using the Apriori algorithm to extract frequent item set associations. Data File preparation is one of the most essential and significant modules of incorporating automation. Therefore the value of this research effort lies in preparing a sample database and how such a sample database can become helpful in construction safety and health management to prevent accidents, as well as the computations of measures of the Apriori algorithm that support decisions regarding construction safety and health provision to construction supervisors and managers are explained

Deep Learning based Market Basket Analysis using Association Rules

Market Basket Analysis (MBA) is a data mining technique assisting retailers in determining the customer's buying habits while making new marketing decisions as the buyer's desire frequently changes with expanding needs; therefore, transactional data is getting large every day. There is a demand to implement Deep Learning (DL) methods to manipulate this rapidly growing data. In previous research, many authors conducted MBA applying DL and association rules (AR) on retail datasets. AR identifies the association between items to find in which order the customer place items in the basket. AR is only used in mining frequently purchased items from retail datasets. There is a gap in classifying these rules and predicting the next basket item using DL on the transactional dataset. This work proposes a framework using AR as a feature selection while applying DL methods for classification and prediction. The experiments were conducted on two datasets, InstaCart and real-life data from Bites Bakers, which operates as a growing store with three branches and 2233 products. The AR classified at 80,20 and 70,30 splits using CNNN, Bi- LSTM, and CNN-BiLSTM. The results considering simulation at both splits show that Bi-LSTM performs with high accuracy, around 0.92 on the InstaCart dataset. In contrast, CNN-BiLSTM performs best at an accuracy of around 0.77 on Bites Bakers dataset.

Implications of the COVID-19 Pandemic on the Organization of Remote Work in IT Companies: The Managers’ Perspective

The study analyses the effects of the COVID-19 pandemic on work models and explores managerial perspectives on remote work compared to stationary work. A survey was conducted among companies, resulting in a sample of respondents. An additional research methodology used to validate the hypotheses was a market basket analysis. The findings indicate a significant change in work models, with a majority of companies adopting fully remote work or hybrid models with remote work as the predominant mode. Managers generally perceive remote work as having a significantly worse outcome compared to stationary work. Concerns about remote work include difficulties in supervising remote workers, maintaining effective communication with the team, and potential negative effects on employee motivation and well-being due to limited interaction. Preferences for work models varied, with a notable proportion favoring fully remote work or hybrid models. Reasons for considering a long-term change to stationary or remote work include coordination needs, control and supervision requirements, physical presence demands, and impacts on organizational culture and atmosphere. Benefits of remote work include employee flexibility, talent attraction, and alignment with employee demands and the labor market. This study provides insights into the evolving work landscape and informs strategies for effectively managing remote work environments.

Understanding Backpacker Behavior Through Market Basket Analysis

The backpacker market is important and diverse for many destinations. Understanding what backpackers do on their trip is important for tourism businesses so they can cater to their needs and wants. Market basket analysis uncovers associations between products by looking for combinations of products that frequently co-occur in transactions. We apply this technique to activities that backpackers do while on vacation in Fiji. This study demonstrates the utility of market basket analysis to better understand backpackers' experiences participating in different activities. Combined with factor analysis and cluster analysis, segmentation based on activities backpackers participate in is more significant than demographic segmentation. In the current study, backpackers can be segmented into active visitors seeking diverse experiences, cultural adventurers, and passive fly and flop tourists. Considering the "fly &amp; flop" segment is the second most common segment, the results suggest backpackers are drifting further away from the original counterculture and towards mass tourism.

월평균 소득에 따른 관광 활동 참여패턴 조사: 장바구니 분석을 적용하여

월평균 소득에 따른 관광 활동 참여패턴 조사: 장바구니 분석을 적용하여

Implementasi Data Maining Untuk Menganalisis Pola Peminjaman Buku Perpustakaan Dengan Menggunakan Metode Apriori

The library is a place where you can find the information andknowledge you need. Library space is a very important means oforganizing book loans because it not only creates space and thenfills it with collections, but also pays attention to how the layout ofthe books is arranged. Therefore, the data is analyzed in order tohelp recommend the layout of books and borrowers can find theappropriate books. To determine the suggestion of borrowedbooks, with the existence of play data can use the a priorialgorithm, a kind of association rule. The market basket analysismethod used by the a priori algorithm to establish associationsbetween various variables allows the system to further analyze andidentify trends related to borrowed books. Based on the results ofthe analysis that has been carried out, it is stated that this a priorialgorithm can be used to display recommendations betweenborrowed books. The support value can show the classification ofbooks borrowed simultaneously while the confidence value canfind out the recommendations for books borrowed.