Market Basket Research Articles

ASCF: Optimization of the Apriori Algorithm Using Spark-Based Cuckoo Filter Structure

Data mining is the process used for extracting hidden patterns from large databases using a variety of techniques. For example, in supermarkets, we can discover the items that are often purchased together and that are hidden within the data. This helps make better decisions which improve the business outcomes. One of the techniques that are used to discover frequent patterns in large databases is frequent itemset mining (FIM) that is a part of association rule mining (ARM). There are different algorithms for mining frequent itemsets. One of the most common algorithms for this purpose is the Apriori algorithm that deduces association rules between different objects which describe how these objects are related together. It can be used in different application areas like market basket analysis, student’s courses selection process in the E-learning platforms, stock management, and medical applications. Nowadays, there is a great explosion of data that will increase the computational time in the Apriori algorithm. Therefore, there is a necessity to run the data-intensive algorithms in a parallel-distributed environment to achieve a convenient performance. In this paper, optimization of the Apriori algorithm using the Spark-based cuckoo filter structure (ASCF) is introduced. ASCF succeeds in removing the candidate generation step from the Apriori algorithm to reduce computational complexity and avoid costly comparisons. It uses the cuckoo filter structure to prune the transactions by reducing the number of items in each transaction. The proposed algorithm is implemented on the Spark in-memory processing distributed environment to reduce processing time. ASCF offers a great improvement in performance over the other candidate algorithms based on Apriori, where it achieves a time of only 5.8% of the state-of-the-art approach on the retail dataset with a minimum support of 0.75%.

Aplikasi Metode Market Basket Analysis dengan Algoritma Apriori untuk Mengetahui Pola Belanja Konsumen pada Online Shop Amerta Fashion

<p>In this era of globalization, society has been facilitated in various ways due to advances in technology, one of which is the use of the internet. One of the goals of the continuous development of technology is to meet human needs. According to a global survey conducted by Nielsen Online in 2009, more than 85% of the world's population has used the internet for buying and selling transactions. The increase in purchases made online, especially during this pandemic period, shows that people have begun to be technology literate. The large number and rapid growth of data often overwhelm business actors in processing data so that they are left unorganized, even though the data obtained can produce useful information to support decisions or assist in determining marketing strategies. This difficulty also occur at Online Shop Amerta Fashion. This study uses one of the data mining methods, namely Market Basket Analysis with a priori algorithm which goes through the stages of data collection, formation of association rules, and concluding. Market basket analysis can determine consumer buying patterns and also find out which items are selling well. The results obtained is that consumers of Online Shop Amerta Fashion didn’t only buy 1 item in a transaction. Boyfriend Jeans Highwaist and Wrap Top Summer are 2 types of items that are popularly purchased by consumers so that they become the most influential items in the overall purchasing transactions from Online Shop Amerta Fashion from January 4, 2021 to June 18, 2021.</p><p><strong>Keywords: </strong>data mining; association rules; market basket analysis; Apriori; Online Shop Amerta Fashion</p>

Exploring the Depths of Market Basket Analysis: A Comprehensive Guide to Transaction Analysis with FP-Growth and Apriori Algorithms

This research investigates the role of data science in understanding customer behavior and enhancing sales, focusing specifically on the application of Apriori and FP-Growth Algorithms at a retail store, Deli Point, in Labuan Bajo. It illuminates the impact of 'rubbish data' on transactional data analysis, emphasizing the need for robust data cleaning procedures to ensure accurate results. Utilizing the faster FP-Growth Algorithm, the study effectively analyzed customer purchasing patterns to identify optimal product combinations for sales improvement. It discovered that 'parsley local' and 'mint flores' items had the highest support with a value of 0.036, indicating that strategic placement of these items together could enhance sales. The rule between chicken leg bone, orange sunkist, and chicken breast boneless was found to have a high confidence value and a lift value higher than 1, implying a higher potential for these items to be sold when positioned near each other. This study contributes to understanding consumer behavior and provides insights for enhancing sales and competitiveness in the retail industry. An association rule involving 'chicken leg bone’, 'orange sunkist', and 'chicken breast boneless' demonstrated high confidence and a lift value above one, suggesting significant sales potential when these items are grouped together. This study not only contributes valuable insights into retail consumer behavior and effective product placement strategies but also underscores the transformative role of data science in optimizing sales and boosting competitiveness in the retail sector.

MARKET BASKET ANALYSIS MENGGUNAKAN ALGORITMA HASH BASED PADA DATA PENJUALAN DI O2 SWALAYAN

O2 Supermarkets have stock of goods available in the warehouse. During this time there is a buildup of goods in the warehouse because there is no system to analyze transaction data so that losses occur. Therefore, a system is needed to manage these goods in order to reduce stocks of goods that are not selling well and make stocks of goods that sell well in warehouses through consumer spending patterns. To analyze data on consumer spending patterns, the Market Basket Analysis Method can be used. This method can be used to analyze consumer spending patterns. The algorithm used is the Hash Based and Association Rule Algorithm because it can overcome the determination of frequent itemsets from k-itemset candidates with a large number and speed up information on consumer spending patterns. With existing data and using this algorithm it can produce a lot of information. The results of this study get 6 Rules with a minimum Confidence of 50% and minimum Support>= 4. From consumer spending patterns that can be used as a strategy to support sales, knowing which items are selling best, placing goods so that they are more efficient in picking up the goods desired by consumers, not only shopping patterns but also more controlled stock of goods.

ATURAN ASOSIASI UNTUK REKOMENDASI STRATEGI PEMASARAN PRODUK KERAJINAN MENGGUNAKAN FREQUENT PATTERN GROWTH

Besides selling equipments and spareparts for two-wheeled vehicles, NTN Racing Store also sells handicraft products made from water hyacinth. Lately, NTN Racing Store facing challenges in selling its products, as evidence by the unpredictable monthly revenue. The reason is insufficiency of customer purchasing patterns understanding. The impacts are the declining of the marketing and sales performance for creative water hyacinth handicrafts products since 2020. The main purpose of this studi is to identify which products are purchase together by customers in one transaction when shopping for water hyacinth products. Market Basket Analysis is a rule that determines associations between various attributes and is a key application in the retail industry. This method is use to analyze past consumer purchasing behavior to determine items that are frequently purchase together. Its purpose is to uncover relationships between products purchased by customers in a single transaction. On the other hand, Frequent Pattern Growth is an alternative algorithm use to identify the most frequently occurring datasets within a collection of data. The testing results of 435 transactional data points from NTN Racing Store, using 1% minimum support value and 25% minimum confidence value, resulting four association rules within lift ratios above 1 which indicates significant relationships among itemsets. From these evaluation results, customer purchasing patterns can be identifying trough the association analysis rules. This information can assist the store owner in determining bundling packages to sell and to devise store sales strategies.

From News to Disinformation: Unpacking a Parasitic Discursive Practice of Czech Pro-Kremlin Media

ABSTRACT This paper examines a “parasitic discourse behavior,” a strategy used by the Czech antisystem media. A working hypothesis about the nature of parasitization was drawn from close reading: the antisystem media, while creating a false impression of mainstream journalism, attempt to reframe events with a set of recurrent associations. This hypothesis was tested with a combination of three quantitative methods: (1) Keyword analysis for identification of prominent topics, which are further analyzed by (2) Companions and (3) Market Basket Analysis (MBA). Companions, a new method comparing occurrences of keywords in time, reveals antisystem’s imitation of the mainstream, and MBA shows its production of distinct associations to frame trendy news items. As a proof of concept, four instances of parasitization were identified during May–August 2020 – topics that attracted public attention for a sufficiently long timespan to come under the radar of our methods: Belarus presidential elections, followed by brutality on the protesters, the police killing of George Floyd in the US, and the subsequent events against systemic racism, the COVID-19 pandemic, and the dispute over the WWII monuments in the Czech Republic. Associations accompanying all these topics weave a set of consistent narratives: anti-West, pro-Kremlin, and a strong anti-Ukrainian stance.

Analysis of Changes in Customers’ Market Basket Across Different Branches of a Chain Store Using Association Rules Technique and Its Impact on Product Placement: A Case Study of a Chain Store in Various Areas of Tehran

Analysis of Changes in Customers’ Market Basket Across Different Branches of a Chain Store Using Association Rules Technique and Its Impact on Product Placement: A Case Study of a Chain Store in Various Areas of Tehran

Paradigmatic alternatives of global development, scientific and technological progress and prospects for the development of civilization in the institutional “physical social economy” models

The article analyzes the models of scientific and technical progress, the application of physical social economy developments in the conditions of globalization processes. The specifics of understanding the essence, forms and content of the strategy for the development of science and socio-economic reforms at different levels are determined. In recent years, the concept of “Physical Social Economy” has occupied a significant theoretical-practical, social-analytical, and at the same time economic-prognostic position among the global development models of national economies, economic forms, international relations, and the world economy, studying the peculiarities and principles of the development of the material sphere (physical) production with the aim of quantitative and qualitative improvement of the filling of the “market basket” on the basis of continuous scientific and technological progress, ensuring the long-term existence of mankind on Earth.

A method for solving cold start problem using market basket analysis

A method for solving cold start problem using market basket analysis

FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops

1Malasowe, Bridget Ogheneovo,, 2Okpako, Abugor Ejaita, 3 , 4Ejeh, Patrick Ogholuwarami, 5Ojugo, Arnold Adimabua & 6 1Department of Computer Science, University of Delta, Agbor, Nigeria 2Department of Cyber Security, University of Delta, Agbor, Nigeria 3Department of Cyber Security, Faculty of Computing, Delta State University of Science and Technology, Ozoro. Nigeria. 4Department of Computer Science, Dennis Osadebay University, Anwai-Asaba, Nigeria 5 Department of Computer Science, Federal University of Petroleum Resources Effurun, Nigeria 6 Department of Computer Science, Federal University of Petroleum Resources Effurun, Nigeria E-mails: 1bridget.malasowe@unidel.edu.ng; 2ejaita.okpako@unidel.edu.ng; 3okpormd@dsust.edu.ng, 4 patrick.ejeh@dou.edu.ng; 5ojugo.arnold@fupre.edu.ng, 6ako.rita@fupre.edu.ng ABSTRACT Transaction data often is a true presentation of consumers’ buying behavior, stored as a set of relational records, which properly harnessed via mining – can help businesses improve their sales volume as a decision support system. Managing such a system can pose many issues to biz such as feature evolution, concept evolution, concept drift, and infinite data length – and often makes it impractical to effectively store such big-data. To curb this, previous studies have assumed data to be stationary in using associative rule mining. This has deprived such systems of the flexibility and adaptiveness required to handle the dynamics of concept drift that characterizes transaction datasets. Our study thus proposes a basket frequent pattern growth trained associative rule mining model to handle large data. The dataset was retrieved from the Delta-Mall Asaba and consists of 556,000 transaction consumer records. The model consists of six-layers, and yields the best result with a 0.1 value for both the confidence and support level(s) at a 94% accuracy, sensitivity of 87%, and a specificity of 32% with a 20-second convergence and processing time. Keywords: Big-Data Analysis, Feature Evolution, Inventory Model, Market Basket Analysis, Businesses, Transactional Data Streams, Concept Drift CISDI Journal Reference Format Malasowe, B.O., Okpako, A.E., Okpor, M.D., Ejeh, P.O., Ojugo, A.A. & Ako, R.E. (2024): FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 2, Pp 15-28. dx.doi.org/10.22624/AIMS/CISDI/V15N2P2-1. Available online at www.isteams.net/cisdijournal

FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops

Transaction data often is a true presentation of consumers’ buying behavior, stored as a set of relational records, which properly harnessed via mining – can help businesses improve their sales volume as a decision support system. Managing such a system can pose many issues to biz such as feature evolution, concept evolution, concept drift, and infinite data length – and often makes it impractical to effectively store such big-data. To curb this, previous studies have assumed data to be stationary in using associative rule mining. This has deprived such systems of the flexibility and adaptiveness required to handle the dynamics of concept drift that characterizes transaction datasets. Our study thus proposes a basket frequent pattern growth trained associative rule mining model to handle large data. The dataset was retrieved from the Delta-Mall Asaba and consists of 556,000 transaction consumer records. The model consists of six-layers, and yields the best result with a 0.1 value for both the confidence and support level(s) at a 94% accuracy, sensitivity of 87%, and a specificity of 32% with a 20-second convergence and processing time. Keywords: Big-Data Analysis, Feature Evolution, Inventory Model, Market Basket Analysis, Businesses, Transactional Data Streams, Concept Drift CISDI Journal Reference Format Malasowe, B.O., Okpako, E.A., Ashioba, N.O., Ejeh, P.O., Ojugo, A.A. & Ako, R.E. (2024): FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 2, Pp 1-14. dx.doi.org/10.22624/AIMS/CISDI/V15N2P2. Available online at www.isteams.net/cisdijournal

A method for solving cold start problem using market basket analysis

A method for solving cold start problem using market basket analysis

Penerapan Data Mining Dalam Menganalisis Pola Belanja Konsumen Menggunakan Market Basket Analysis

Currently, almost every activity is related to data. in the business sector, daily sales transaction data stored in the database system will always increase and accumulate. The existing data is only used as an archive by the shop owner so that it has an impact on sales strategies that are not implemented well, even though the existing data can be processed into information to determine the layout of goods so that it has an impact on increasing the occurrence of impulse buying, increasing or maintaining turnover, and minimizing product waste. accumulate until it expires which can be detrimental to the shop.The aim of this research is to find consumer shopping patterns using Marker Basket Analysis. This research method is called market basket analysis or also called association rules, which is a data mining technique for finding patterns that often appear simultaneously in transaction data, so that it can be used as a method for finding information about what kinds of goods are frequently used. purchased by consumers simultaneously. The results of this research, based on data analysis using the Rapidminer application, found 25 associative relationships or rules with a lift ratio value of more than 1, these rules become a reference in determining the layout of goods. Providing recommendations for layout changes aims to make it easier for consumers to shop, increase the possibility of impulse buying by consumers, and maximize product display, thereby reducing the accumulation of goods in the Purnama Store Warehouse.

A scalable, distributed framework for significant subgroup discovery

Subgroup discovery is a supervised data mining technique having many applications in medical domains, market basket analysis, and social media analysis. It helps in mining subgroups (or patterns) with a high association to a target property, measured using a quality function. However, the process is computationally intensive as it is necessary to go through the search space of all subgroups to find the top-k interesting ones w.r.t. the quality function. Further, as we verify many associations, it is quite possible that a certain level of association might be achieved by chance. To address this issue, the state-of-the-art TopKWY algorithm employs permutation testing to control false discoveries. Still, testing multiple subgroups against thousands of permuted target labels further increases computational complexity. Additionally, TopKWY is limited to a specific quality function and lacks a parallel/distributed implementation to handle scalability challenges. In this paper, we propose a parallel and distributed framework for subgroup discovery named ParaDiS that extends permutation testing to a broader class of quality functions. ParaDiS scales to large datasets while effectively controlling the false discovery rate. It features different optimizations to reduce communication/computation overheads and a distributed best-first search strategy to improve pruning across different workers. We compare its performance for several real-world datasets and achieve an order of magnitude reduction in the execution time compared to the sequential approach.

Identifying Product Bundle from Market Basket Analysis

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 products and services in a comprehensive package offered by the seller. Additionally, bundles are favorable to some individuals due to their ability to mitigate compatibility risks between various components. From the seller’s view, adopting bundling can lead to increased sales and a broader customer base. Bundling facilitates the attraction of buyers, while simultaneously raising awareness and acceptance of newly released products...The scope of this project, titled "Identifying Product Bundles from Sales Data using Market Basket," a highly performant model has been developed to aid in the identification of product bundles and market basket determination. Recognizing the multitude of prediction challenges during product sales, this project utilizes historical sales data to predict optimal product bundles. Leveraging a dataset obtained from Instacart, the project incorporates clustering and analysis processes. By thoroughly analyzing the data, the model can predict the most effective product bundles, enabling the selling company to boost its sales potential. This project specifically caters to e-commerce websites seeking to address product bundling and market basket analysis challenges. It provides a valuable platform for applying various techniques to solve problems associated with product bundling, generating comprehensive theoretical and practical resources for research-based studies. Ultimately, this project empowers businesses to make more reliable predictions about the future, enhancing their decision-making processes.

A data mining approach for traffic accidents, pattern extraction and test scenario generation for autonomous vehicles

To effectively fight against traffic accidents, it is of great importance to analyse and understand the conditions that are linked with accidents. Such an analysis can serve as the basis to (i) develop reactive measures by finding the links between the pre-accident conditions (ii) devise proactive strategies that will prevent the occurrence of accidents by making the vehicles safer. This paper contributes to advancement of both approaches. For (i), one needs to identify the patterns in accidents. For (ii), introduction of Connected and Automated Vehicles (CAVs) is a promising solution. However CAVs need to be tested under numerous traffic scenarios to prove their safety before their deployment on public roads. This necessitates a great demand for high quality test scenarios for CAVs. This paper achieves two goals. First, it analyses the past traffic accidents (UK’s STATS19 database) to identify trends in the heterogeneous accident data and unravel the relationships between pre-accident conditions. This is done using a clustering algorithm (ROCK). Seven distinct large clusters emerge as a result. Each of these clusters are then further analysed for their meaning using the frequency analysis and geometric analysis. Secondly the paper underpins the proactive route (ii) by systematically developing, using the information in each cluster, test-case scenarios for CAVs which reflect the risk-prone conditions of the respective clusters. This is done using a data mining method (Market Basket algorithm) and further geometric interpretation of clusters. This way explicit scenarios are developed carrying the characteristics of the clusters that they come from.

COMPARISON OF MARKET BASKET ANALYSIS METHOD USING APRIORI ALGORITHM, FREQUENT PATTERN GROWTH (FP- GROWTH) AND EQUIVALENCE CLASS TRANSFORMATION (ECLAT) (CASE STUDY: SUPERMARKET “X” TRANSACTION DATA FOR 2021)

The retail industry continues to grow and develop in Indonesia. The retail sector as a provider of goods used in everyday life has long started digital transformation in its business. Digital technology helps the retail industry collect valuable customer data. Business analytic is the use of data, information technology and statistical analysis, which is used to obtain information about a business and make decisions based on facts. Business analytic turns data into steps or actions in the context of making business decisions. Consumer needs and purchasing behavior can be predicted with big data-based technology. Association Rule is a technique in data mining to find the relationship between items in an item set combination. One of the utilizations of the association rule method is market basket analysis. Algorithms that can be used to analyze consumer purchasing patterns include the Apriori algorithm, Frequent Pattern Growth (FP-Growth) which represents a database structure in a horizontal format, and the Equivalence Class Transformation (ECLAT) algorithm which represents a vertical data format. In addition, this research will first analyze the complexity of the algorithm based on the time complexity in running the algorithm. This analysis uses these three algorithms, which are applied to Supermarket "X" transaction data in 2021, namely 136,202 transactions. The measure of goodness that is used to find out the best algorithm uses support and confidence values. The results show that the ECLAT algorithm is the most superior algorithm compared to the others based on the execution time required by the algorithm. The support value used in forming associations in the ECLAT algorithm is 1%, resulting in 19 rules. From the results of these rules, the highest support value was generated by the purchase of Indomie goreng special and Indomie ayam bawang, where as many as 1,362 shopping transactions bought these two items together or 2.71% of the total transactions.

Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland.

There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.

Analisis Pola Konsumen Dalam Bertransaksi Bisnis di Bengkel Resmi AHASS Total Honda Motor

The accumulated data, which consists of facts and transaction events in a business, should be processed and utilized for the progress of business development. Currently, the data owned by AHASS THM has not been optimized and further processed to provide broader benefits, such as promotion and forming loyal AHASS customers. The objective of this research is to analyze the existing transaction data to identify consumer transaction patterns at AHASS THM. The research methodology used is Market Basket Analysis (MBA), a method for analyzing consumer transaction data by finding associative relationships between different items in the consumer's shopping cart. By applying a minimum parameter limitation of support = 0.001, confidence = 0.8, and sorting based on the magnitude of the confidence parameter, 62 associative rules of consumer transaction patterns in AHASS THM business were obtained. By selecting the top 10 associative rules based on the highest confidence values, generally, these associative rules have a confidence parameter greater than 0.95 or 95%. Additionally, there are 3 associative rules with a confidence value of 1 or 100%, indicating that consumers will purchase Bearing Needle 20x29x218 after buying Bearing Ball 6902U, or a combination of Bearing Ball 6902U with CVT Grease 10 gr or Oli MPX2 0.8 lt.

Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System

Recommender systems position themselves as powerful tools in the support of relevance and personalization, presenting remarkable potential in the area of marketing. The cold-start customer problematic presents a challenge within this topic, leading to the need of distinguishing user features and preferences based on a restricted set of transactional information. This paper proposes a hybrid recommender system that aims to leverage transactional and portfolio information as indicating characteristics of customer behaviour. Four independent systems are combined through a parallelised weighted hybrid design. The first individual system utilises the price, target age, and brand of each product to develop a content-based recommender system, identifying item similarities. Secondly, a keyword-based content system uses product titles and descriptions to identify related groups of items. The third system utilises transactional data, defining similarity between products based on purchasing patterns, categorised as a collaborative model. The fourth system distinguishes itself from the previous approaches by leveraging association rules, using transactional information to establish antecedent and precedence relationships between items through a market basket analysis. Two datasets were analysed: product portfolio and transactional datasets. The product portfolio had 17,118 unique products and the included 4,408,825 instances from 2 June 2021 until 2 June 2022. Although the collaborative system demonstrated the best evaluation metrics when comparing all systems individually, the hybridisation of the four systems surpassed each of the individual systems in performance, with a 8.9% hit rate, 6.6% portfolio coverage, and with closer targeting of customer preferences and smaller bias.