Market Basket Research Articles

Dietary exposure to arsenic species in Japan in 2019 using a total diet study based on composite sample with market basket approach at the national level

To estimate the mean dietary exposure to total As (tAs) and some arsenic species [inorganic arsenic (iAs), methylarsonic acid (MMAs) and dimethylarsinic acid (DMAs), and arsenobetaine (AsB)] across the entire Japanese population (≥1 year old), a national total diet study (TDS) based on composite sample with a market-basket approach was conducted in Japan from 10 regions throughout Japan in 2019. Mean dietary exposure to iAs of 0.251 μg kg−1 day−1 showed a slightly lower value compared to the health-based guideline value of 0.30 μg kg−1 day−1. The calculated margin of exposure of 1.2–31.9 were lower or similar to the uncertainty factors of 30. Therefore, the possibility of a risk to Japanese people cannot be excluded. This study indicated that consumption of rice and rice products contributed to 64 % of the dietary exposure to iAs. Furthermore, results showed that dietary exposure to tAs had been increasing gradually since 2004, while that to iAs remained steady since 2014. These data indicate the importance of continuing to survey the dietary exposure to total As and As species.

Shock determination in a two-stage decision-making model: The case of COVID-19 in Colombia.

The contributions of this paper are as follows: (a) proposing a two‐stage model to study whether an event has temporary or permanent effects on the probability of choosing a good within a market basket that traditional decision theory cannot explain and (b) studying the effects of COVID‐19 on consumers' decisions in Colombia. Findings suggest that the pandemic has transitory effects on preferences in the short run. If it lasts longer, it could induce to permanent changes. Thus, this model can be used to analyze the temporary or permanent effects of any event, regardless of its nature or geographical region, on consumer's decision.

PERBAIKAN TATA LETAK PASAR INDUK TRADISIONAL DI SANGATTA DENGAN METODE MARKET BASKET ANALYSIS (MBA)

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 consumers at the same time. In addition, the application of this method can provide convenience for consumers in shopping, because goods that are usually purchased simultaneously are nearby. In addition, it can improve efficiency in conducting promotions. Products purchased should not be produced simultaneously, promoting one product can increase sales of other products that are usually purchased simultaneouslyThis study uses the Market Basket Analysis (MBA) method. Product information that dominates sales can be used as a reference to determine consumer habits in making purchases. The habit in question is any product that consumers buy at the same time. The use of Market Basket Analysis (MBA) is expected to cause impulse buying/purchases that were not planned. In addition to using Market Basket Analysis (MBA), Activity Relationship Chart (ARC) is also used as a tool to get layout results that are by consumer habits in shopping and with government regulations. Keywords : Product layout, Market basket analysis, Activity relationship chart.

TF-COMB – Discovering grammar of transcription factor binding sites

Cooperativity between transcription factors is important to regulate target gene expression. In particular, the binding grammar of TFs in relation to each other, as well as in the context of other genomic elements, is crucial for TF functionality. However, tools to easily uncover co-occurrence between DNA-binding proteins, and investigate the regulatory modules of TFs, are limited. Here we present TF-COMB (Transcription Factor Co-Occurrence using Market Basket analysis) - a tool to investigate co-occurring TFs and binding grammar within regulatory regions. We found that TF-COMB can accurately identify known co-occurring TFs from ChIP-seq data, as well as uncover preferential localization to other genomic elements. With the use of ATAC-seq footprinting and TF motif locations, we found that TFs exhibit both preferred orientation and distance in relation to each other, and that these are biologically significant. Finally, we extended the analysis to not only investigate individual TF pairs, but also TF pairs in the context of networks, which enabled the investigation of TF complexes and TF hubs. In conclusion, TF-COMB is a flexible tool to investigate various aspects of TF binding grammar.

Strategic Insights from Optimized Market Basket Analysis in Wordle Game Environment and its Application in Retail

Strategic Insights from Optimized Market Basket Analysis in Wordle Game Environment and its Application in Retail

Dietary Exposure of the Japanese General Population to Elements: Total Diet Study 2013-2018.

Some countries have conducted a total diet study (TDS) focused on the estimation of specific trace elements. Although some results of a Japanese TDS examining trace elements were published, there have been no reports of a nationwide TDS across Japan over a multi-year period to estimate the level of exposure to multiple elements. In the present study, a TDS using a market basket approach was performed to estimate the dietary exposure levels of the general population of Japan to 15 elements, including aluminum (Al), total arsenic (tAs), boron (B), barium (Ba), cadmium (Cd), cobalt (Co), chromium (Cr), total mercury (THg), molybdenum (Mo), nickel (Ni), lead (Pb), antimony (Sb), selenium (Se), tin (Sn), and uranium (U). Samples prepared in eight regions across Japan over a 6-year period were analyzed using validated methods. The robust mean exposure estimates for Al, tAs, B, Ba, Cd, Co, Cr, THg, Mo, Ni, Pb, Sb, Se, Sn, and U were 48, 4.2, 29, 8.6, 0.35, 0.17, 0.49, 0.14, 4.2, 2.8, 0.15, 0.022, 1.8, 0.10, and 0.021 μg/kg body weight/day, respectively. Although the variability in exposure estimates varied greatly from element to element, the relative standard deviations calculated from the robust means and robust standard deviations were ≤ 50% for all elements except Sn. Compared against the health-based guidance values, none of the robust and precise estimates obtained for the target elements would be associated with urgent health risk concern. In addition, the estimated exposure levels were generally in agreement with previously reported estimates, indicating that health risks associated with exposure to these elements have not changed markedly nationwide in Japan in recent years.

Simultaneous Topology and Loss Tomography via a Modified Theme Dictionary Model

As a technique to investigate behaviours of a computer network with low operational cost, network tomography has received considerable attentions in recent years. Most studies in this area assume that the topology of the network of interest is known, and try to propose computationally and/or statistically efficient methods to estimate link-level properties such as loss rate, delay distribution, bandwidth etc., or global traffic properties such as point-to-point traffic matrix. Little progresses have been made for scenarios when topology of the target network is unknown, although it is often the case in many practical applications. The few published works for topology tomography resolved the problem primarily by clustering analysis, which works for tree-like networks only and often suffers from unstable performance for large networks of complicated structure. In this article, we study the classic problem of network tomography from a new perspective. By connecting the problem of topology tomography to the classic machine learning problem of “market basket analysis,” we find that simultaneous topology and loss tomography can be achieved by discovering association patterns of loss records collected at receivers, which can be efficiently resolved with light modifications of a recently developed statistical method known as the “theme dictionary model”. Both theoretical analysis and simulation studies demonstrate that the proposed approach enjoys improved effectiveness for networks of tree as well as general topology with slightly higher computational costs.

Market basket analysis of administrative patterns data of consumer purchases using data mining technology

Food is the ingredient that enables people to grow, develop, and achieve. For this reason, food quality and types of food must be considered so that they are safe for consumption and managed. Some plant-based foodstuffs are often processed and consumed by the community, even the most needed in food processing. In this case, the research was carried out using data mining with market basket analysis algorithms to obtain very valuable information to decide the inventory of the type of material needed. Market Based Analysis method is used to analyze all data and create patterns for each data. One method of Market Based Analysis in question is the association rule with a priori algorithm. This algorithm produces sales transactions with strong associations between items in the transaction which are used as sales recommendations that help users (owners) get recommendations when users see details of the itemset purchased. From the results of the trials in this study, it was found that the greater the minimum support (minsup) and minimum confidence (minconf), the less time it takes to produce recommendations and the fewer recommendations are given, but the recommendations given come from transactions that often appear.

Utility-frequency analysis using customer market basket data in the retail sector

Utility-frequency analysis using customer market basket data in the retail sector

Identification of Traffic Accident Patterns via Cluster Analysis and Test Scenario Development for Autonomous Vehicles

Increased safety is one of the main motivations for traffic research and planning. The arduous task has two components: (i) improving the existing traffic policies based on a good understanding of risk factors related to trends in traffic accidents, and (ii) underpinning the emerging technologies that will advance the safety of vehicles. For the latter route, the introduction of connected and automated vehicles (CAVs) is a promising option as CAVs can potentially reduce the number of accidents. However, to reap their benefits, they need to be introduced in a safe manner and tested for their ability to safely deal with risky scenarios. Unfortunately, the identification of such test scenarios remains a key challenge for the industry. This study contributes to increased safety by (i) analyzing UK’s STATS19 accident data to identify patterns in past traffic accidents, and (ii) utilizing this information to systematically generate scenarios for CAV testing. For task (i), the patterns in the accidents were identified in terms of static and time-dependent internal and external factors. For this purpose, the study employed a clustering algorithm, COOLCAT, which is particularly suitable for dealing with high-dimensional categorical data. Six different clusters emerged naturally as a result of the algorithm. To interpret the clusters, we applied a frequency analysis to each cluster. The frequency tests showed that in each cluster, certain distinct real-world situations were represented more significantly compared to the non-clustered reference case, which are the markers of each cluster. The second task (ii) complemented the first task by synthesizing the relationships between attributes. This was done by association rule mining using the market basket analysis approach. The method enabled us to develop, drawing from the characteristics of the clusters, non-trivial test scenarios that can be used in the testing of CAVs, especially in virtual testing.

A Survey on Methods and Applications of Intelligent Market Basket Analysis Based on Association Rule

The market trends rapidly changed over the last two decades. The primary reason is the newly created opportunities and the increased number of competitors competing to grasp market share using business analysis techniques. Market Basket Analysis has a tangible effect in facilitating current change in the market. Market Basket Analysis is one of the famous fields that deal with Big Data and Data Mining applications. MBA initially uses Association Rule Learning (ARL) as a mean for realization. ARL has a beneficial effect in providing a plenty benefit in analyzing the market data and understanding customers’ behavior. An important motive of using such techniques is maximizing the business profit as well as matching the exact customer needs as closely as possible. In this survey paper, we discussed several applications and methods of MBA based on ARL. Also, we reviewed some association rule learning measurements including trust, lift, leverage, and others. Furthermore, we discuss some open issues and future topics in the area of market basket analysis and association rule learning.

Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework

Mining high utility sequential patterns is observed to be a significant research in data mining. Several methods mine the sequential patterns while taking utility values into consideration. The patterns of this type can determine the order in which items were purchased, but not the time interval between them. The time interval among items is important for predicting the most useful real-world circumstances, including retail market basket data analysis, stock market fluctuations, DNA sequence analysis, and so on. There are a very few algorithms for mining sequential patterns those consider both the utility and time interval. However, they assume the same threshold for each item, maintaining the same unit profit. Moreover, with the rapid growth in data, the traditional algorithms cannot handle the big data and are not scalable. To handle this problem, we propose a distributed three phase MapReduce framework that considers multiple utilities and suitable for handling big data. The time constraints are pushed into the algorithm instead of pre-defined intervals. Also, the proposed upper bound minimizes the number of candidate patterns during the mining process. The approach has been tested and the experimental results show its efficiency in terms of efficiency, memory utilization, and scalability.

Measuring the Impacts of Consumers’ Demographics on Spending Propensity and Expenses on Firms Profitability using Market Basket Optimization Model

Measuring the Impacts of Consumers’ Demographics on Spending Propensity and Expenses on Firms Profitability using Market Basket Optimization Model

Comparative Analysis of Apriori Algorithm and Hash-Based Algorithm in Market Basket Analysis

Grocery stores are now experiencing competition in the business world that is getting tighter, making businesses have to think hard in developing strategies to face competition. In developing strategies that benefit companies can take advantage of information technology. Information technology can help business companies in conducting their business. In this case, business companies can utilize the data generated by information systems to assist in decision making if processed correctly; such data can produce valuable information. Data Mining is the process of using artificial intelligence mathematical statistics techniques and Machine Learning to extract and identify useful information and related knowledge from various large databases/ Data Warehouse (Kennedi Tampubolon, 2013). In this study, researchers used a priori algorithm and Hash-Based Algorithm to determine consumer spending patterns or consumer shopping cart data used as much as 1023 transaction data with a minimum value of 0.03 and Confidence of 0.5. This study resulted in an Apriori algorithm producing seven rules and forming a combination of 2 items with a rule strength of 13.14% and accuracy of 92.80%. Hash-Based Algorithm 7 Rule developed as many as two itemsets with a rule strength of 14.35%and formed an accuracy of 107.76%. From the results of the algorithm, comparison can be concluded that Hash-Based Algorithm is better than Apriori algorithm

Determination and probabilistic health risk assessment of heavy metals in widely consumed market basket fruits from Dhaka city Bangladesh

ABSTRACT Heavy metals contamination of fruits and vegetables is a growing concern to the public and policymakers in developing countries. This study investigated the extent of heavy metal contamination in widely consumed grape, apple, orange, banana, and pomegranate from supermarkets in Dhaka, Bangladesh, using atomic absorption spectroscopy (AAS) to assess the health risks to humans. Of the total 80% of banana and 60% of pomegranate samples, the mean lead (Pb) concentrations were 0.608 and 0.164, respectively, exceeding the maximum allowable concentration (MAC = 0.1) set by FAO/WHO and EU regulations, while the remaining fruit samples were below the limit. The Single-factor pollution index (PI) of Pb in bananas (6.08) and pomegranate (1.64) was higher than the reference value (PI>1), indicating the lower quality of fruits due to the higher amount of lead contents. The calculated incremental lifetime cancer risks (ILCRs) of cadmium (Cd) and Pb in banana, apple, grape, orange, and pomegranate for both children and i adults were exceeded the threshold limit (ILCR>10−6), which suggests that the consumption of heavy metal contaminated fruits poses potential cancer risks to the human health. In contrast, target hazard quotients (THQs) and chronic hazard index (HIs) values of all heavy metals were in the range of (THQ: 2.4 × 10−6–0.76; 6.5 × 10−7–0.20) and (HI:0.03–0.49; 0.01–0.13), respectively, for children and adults, which were below the acceptable limit (THQ<1), indicating no potential non-cancer risk for both consumer groups. These findings might help the policymakers and regulatory bodies concerned to apply control measures through monitoring the quality of locally grown or imported fruits available in supermarkets for human consumption.

Development of the Purchase to Plate Crosswalk and Price Tool: Estimating prices for the National Health And Nutrition Examination Survey (NHANES) foods and measuring the healthfulness of retail food purchases

The Purchase to Plate Crosswalk (PPC) enables researchers to measure the healthfulness of retail (grocery store) food purchases using the Healthy Eating Index. The Purchase to Plate Price Tool estimates prices for the foods reported by participants in the What We Eat in America study, the dietary component of the National Health and Nutrition Examination Survey (WWEIA/NHANES), and the Purchase to Plate Ingredient Tool breaks the prepared foods into generalized ingredient quantities. This study provides the background and methods used to develop these novel tools. The PPC uses a combination of probabilistic and manual matching to link the scanner data to the USDA Food and Nutrient Database for Dietary Studies (FNDDS). These tools were developed by USDA to meet the 2018 Farm Act requirement that USDA update the market basket of the Thrifty Food Plan (TFP) every five years, starting in 2022. The evidence-based update requires current prices and research on the cost and healthfulness of foods purchased by U.S. consumers. USDA plans to update these tools periodically to enable future updates of the TFP market basket. The National Average Prices for NHANES are available to researchers upon request.

Electronic petitioning as online collective action: Examining the e-petitioning behavior of an extremist group in we the people

This study aims to reveal patterns of e-petition co-signing behavior that are indicative of the mobilization of online “communities” engaging in collective action to express policy preferences on We the People (WtP), the first web-enabled US government petitioning system initiated by Obama. This Internet-based tool allowed users to petition the Obama Administration and solicit support for policy suggestions. Using petition data from WtP, this case study examines a set of 125 petitions that were created by individuals that are associated with a white supremacist group called The White Genocide Project (The White Genocide Project has recently changed their name to Fight White Genocide). Using data mining techniques, namely market basket analysis and social network analysis, I found evidence of the mobilization of “communities” of an extremist group of white supremacists who systematically and strategically used the WtP platform to broadcast their message by creating and co-signing petitions every month for almost four years.

Multi-core parallel algorithms for hiding high-utility sequential patterns

High-utility sequential pattern mining (HUSPM) can be applied in many applications such as retail, market basket analysis, click-stream analysis, healthcare data analysis, and bioinformatics. HUSPM algorithms discover useful information from data. However, looking at the dark side, the sensitive patterns can also be disclosed by the competitors, who use a HUSPM algorithm on the leaked data. Therefore, high-utility sequential pattern hiding (HUSPH) is used to protect the privacy information from HUSPM algorithms. This paper proposes three algorithms named High Utility Sequential Pattern Hiding Using Pure Array Structure (USHPA), High Utility Sequential Pattern Hiding Using Parallel Strategy (USHP), and High Utility Sequential Pattern Hiding Using Random Distribution Strategy (USHR) for hiding high-utility sequential patterns on quantitative sequence datasets. These algorithms use a proposed data structure named Pattern Utility Set for Hiding (PUSH) to speed up the hiding process. We also introduce a metric called Privacy Factor to evaluate the quality of hiding results. The comparative experiments were conducted on real datasets to evaluate the performance of the proposed algorithms in terms of runtime, memory consumption, scalability, missing cost, and privacy factor. Results show that the proposed algorithms can efficiently sanitize the input datasets, and they outperform the compared algorithms for all metrics.

A Market Basket Assessment: Prices and Availability of Healthy Foods Across SNAP-Authorized Food Outlets in Counties With High Obesity Rates in Mississippi.

IntroductionThe Mississippi Delta is predominantly rural and ranks among the US regions with the highest obesity rates. Throughout the US, rural and low-income communities have limited access to healthy foods. Given the interrelation between the quality of the food environment and the healthfulness of diets and obesity rates, the food environment is an important public health concern in these communities.MethodsWe conducted a retail assessment in July 2019 in the Delta region of Mississippi and evaluated prices and availability of healthy foods at Supplemental Nutrition Assistance Program–accepting retail establishments using the validated Market Basket Assessment Tool. We used regression analysis to identify differences in prices and availability of healthy foods across food retail formats.ResultsThe healthy foods availability and quality score for convenience stores, which comprise the highest proportion of store formats in the region, was 70% lower than for supermarkets. Compared with the prices at supermarkets, the prices at convenience stores were 48% higher for grains, 35% higher for fruit and vegetables, 73% higher for meats, and 95% higher for beans, seeds, and nuts. The healthfulness of foods available at dollar stores was also lower than the healthfulness at supermarkets, but prices were generally similar.ConclusionThe availability of supermarkets and grocery stores was limited in the study area, but the concentration of convenience stores was high. Overall, access and affordability of healthy foods were restricted in the counties studied; these findings are useful for intervention development.

Adjusting cyber insurance premiums based on frequency in a communication network

This study compares cyber insurance premiums with and without a communication network effect frequency. As a cybersecurity factor, the frequency in a communication network influences the speed of cyberattack transmission. It means that a network or a high activity node is more vulnerable than a network with low activity. Traditionally, cyber insurance pricing considers historical data to set premiums or rates. Conversely, the network security level can evaluate using the Monte Carlo simulation based on the epidemic model. This simulation requires spreading parameters, such as infection rate, recovery rate, and self-infection rate. Our idea is to modify the infection rate as a function of the frequency in a communication network. The node-based model uses probability distributions for the communication mechanism to generate the data. It adopts the co-purchase network formation in market basket analysis for building weighted edges and nodes. Simulations are used to compare the initial and modified infection rates. This paper considered prism and Petersen graph topology as case studies. The relative difference is a metric to compare the significance of premium adjustment. The results show that the premium for a node with a low level in a communication network can reach 28.28% lower than the initial premium. The premium can reach 20.99% lower than the initial network premium for a network. Based on these results, insurance companies can adjust cyber insurance premiums based on computer usage to offer a more appropriate price.