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This thesis provides a novel view of celebrity fandom through the lens of big social data, while at the same time exploring the opportunities and challenges of using digital traces from social media for sociological research. The first chapter provides a sociological framing of celebrity, a short, joint history of celebrity and the media, and a discussion of the revolutionary role that social media and its platforms played in celebrity culture. Finally, it attempts to bridge theories related to celebrity's role in society with research on "lifestyle politics", "polarization", "taste cultures" and "lifestyle enclaves". The second chapter serves as an introduction into big social data and digital trace data. First as a socio-technological phenomenon, then as a research tool. It covers its historical and current availability and discusses its epistemological and practical opportunities, limits and dangers. Finally, it introduces Facebook pagelikes as a valuable source of information on lifestyle politics. Chapter three is an exploration of Facebook digital traces of 90k celebrity followers. It asks whether celebrity preferences are related to differences in various aspects of life, including politics, leisure or cultural consumption. Methodologically, it covers combining data from APIs with web scraping and...

Market Basket Analysis is an observational data mining methodology to investigate the consumer buying behavior patterns in retail Supermarket. It analyzes customer baskets and explores the relationship among products that helps retailers to design store layouts, make various strategic plans and other merchandising decisions that have a big impact on retail marketing and sales. Frequent itemsets mining is the first step for market basket analysis. The association rules mining uncovers the relationship among products by looking what products the customers frequently purchase together. In retail marketing, the transactional database consists of many itemsets that are frequent only in a particular season however not taken into consideration as frequent in general. In some cases, association rules mining at lower data level with uniform support doesn't reflect any significant pattern however there is valuable information hiding behind it. To overcome those problems, we propose a methodology for mining seasonally frequent patterns and association rules with multilevel data environments. Our main contribution is to discover the hidden seasonal itemsets and extract the seasonal associations among products in additionally with the traditional strong regular rules in transactional database that shows the superiority for making season based merchandising decisions. The dataset has been generated from the transaction slips in large supermarket of Bangladesh that discover 442 more seasonal patterns as well as 1032 seasonal association rules in additionally with the regular rules for 0.1% minimum support and 50% minimum confidence.

Marketplace platforms gather and store data on each activity of their users to analyze their customer purchase behavior helping to improve marketing activities such as product placement, cross-selling, or customer retention. Market basket analysis (MBA) has remained a valuable data mining technique for decades for marketers and researchers. It discovers the relationship between two products that are frequently purchased together using association rules. One of the issues with this method is its strict focus on binary relationships, which prevents it from examining the product relationships from a broader perspective. The researchers presented several methods to address this issue by building a network of products (co-purchase networks) and analyzing them with network analysis techniques for purposes such as product recommendation and customer segmentation. This research aims at segmenting products based on customers’ purchase patterns. We discover the patterns using the Stochastic Block Modeling (SBM) community detection technique. This statistically principled method groups the products into communities based on their connection patterns. Examining the discovered communities, we segment the products and label them according to their roles in the network by calculating the network characteristics. The SBM results showed that the network exhibits a community structure having a total of 309 product communities, 17 of which have high betweenness values indicating that the member products play a bridge role in the network. Additionally, the algorithm discovers communities enclosing products with high eigenvector centralities signaling that they are a focal point in the network topology. In terms of business implications, segmenting products according to their role in the system helps managers with their marketing efforts for cross-selling, product placement, and product recommendation.

The increasingly fierce competition in the business world lately has forced business actors to have the right strategy to increase their product sales, one of which is to determine sales patterns. However, along with the amount of sales transaction data that is carried out every day, it makes it difficult for business actors to analyze existing sales patterns. So we need a method that can help determine sales patterns from even large transaction data, one of which uses the Market Basket Analysis method which uses customer data that has been stored in the database to find new information in it. By utilizing the sales transaction data of consumers supported by the Market Basket Analysis method, it is possible to determine the right marketing tactics for business actors so as to increase sales

Market Basket Analysis (MBA) is a method for determining the association between entities, and it has often been used to study the association between products in a shopping basket. Trained Computer vision models are able to recognize objects in photos so accurately that it can even outperform humans in some instances. This study shows that combining objective detection techniques with market basket analysis can assist Stores/Kirana in organizing the products effectively. With the use of MBA and Object detection, we formulated recommendations for store arrangements along with putting a recommendation engine on top to help shoppers. After deploying this to local Kirana stores, the Kirana store was able to see an increase of 7% in the sale. The recommendation engine performed better than just the domain knowledge of the kirana store.

Peningkatan pelayanan merupakan aspek terpenting dalam proses bisnis salah satunya adalah dibidang penjualan makanan dan minuman. Oriental cafe merupakan salah satu resto yang bergerak dibidang food and beverage. Penjualan tahun 2020 mengalami penurunan sebesar 13% karena menurunya minat pelanggan terhadap pemberian paket produk yang mempengaruhi pelayanan pelanggan. Pemberian sistem paket dapat dilakukan dengan memperlajari kebiasaan pelanggan dalam membeli makanan dan minuman. Salah satu alternatif yang digunakan adalah dengan Data Mining yaitu market basket analysis, market basket analysis dapat menciptakan pola produk yang sering dibeli oleh pelanggan sehingga dapat menjadi aturan pembuatan sistem paket produk. Pada penelitian ini dilakukan pengujian data menggunakan associaton rule dengan algortima apriori. Algoritma apriori memiliki keunggulan karena lebih sederhana dalam pengolahan data dan bisa menangani data yang cukup besar. Dari hasil pembahasan dan analisis data yang dilakukan dapat disimpulkan bahwa dengan penerapan algoritma apriori dalam menentukan kombinasi antar itemset dengan minimum support 30% dan minimum confidence 70% ditemukan 1 aturan asosiasi, dimana yang memiliki nilai support dan confidence tertinggi adalah jika pelanggan melakukan pemesanan menu Mineral, Fish dan Rice secara bersamaan dengan nilai support 30% dan nilai confidence 100%. Dengan demikian, jika terdapat pelanggan membeli mineral maka kemungkinan pelanggan tersebut membeli fish, rice adalah 100%.

KAOCHEM Sinergi Mandiri Cooperative is a cooperative that provides various kinds of basic needs such as basic foodstuffs that can meet the needs of its members. The cooperative transaction data is only stored as a report. Association rules are a method in data mining that functions to identify items that have a value that is likely to appear simultaneously with other items. One implementation of the association method is Market Basket Analysis. The data used are transaction data for November 2019. Data mining is one of the processes or stages of the KDD method. The data mining process is carried out using the FP-Growth algorithm, which is one of the algorithms for calculating the sets that often appear from data. Researchers analyzed transaction data using the Rapid Miner Studio tools. In the data mining process using FP-Growth the researcher determines a minimum support value of 3% and a minimum confidence of 50%. The association process using these values ​​produces 3 strong rules, namely if ades 350 ml, then fried / lontong with a support value of 0.030 and confidence 0.556 and if fried st, then fried / lontong with a support value of 0.048 and confidence 0.639, and if nasi uduk / bacang , then fried / rice cake with a support value of 0.031 and confidence 0.824. The results of the association rules can be applied using one of the marketing techniques, namely cross-selling to increase the sales of the cooperative.

Technological development has engaged educational institutions in fierce global competition. To be competitive in meeting the changing needs of today’s student population, educational institutions find it imperative to prioritize student retention efforts and to develop strategies that interact and serve students more effectively in providing them more value and service. In this research we proposed a three-phase-six-stage adaptive data mining development life cycle, and we applied the affinity analysis to this methodology in identifying more than 400 association relationships with student retention, refining iteratively the association rule set down to less than 30 rules, and developing useful strategic implications regarding how the important factors were associated with a student’s decision. This set of implications and factors could then be integrated into the development of strategies for student retention

High Utility Itemset Mining (HUIM) is one of the most investigated tasks of data mining. It has broad applications in domains such as product recommendation, market basket analysis, e-learning, text mining, bioinformatics, and web click stream analysis. Insights from such pattern analysis provide numerous benefits, including cost cutting, improved competitive advantage, and increased revenue. However, HUIM methods may discover misleading patterns as they do not evaluate the correlation of extracted patterns. As a consequence, a number of algorithms have been proposed to mine correlated HUIs. These algorithms still suffer from the issue of the computational cost in terms of both time and memory consumption. This paper presents an algorithm, named Efficient Correlated High Utility Pattern Mining (ECoHUPM), to efficiently mine the high utility patterns having strong correlation items. A new data structure based on utility tree (UTtree) named CoUTlist is proposed to store sufficient information for mining the desired patterns. Three pruning properties are introduced to reduce the search space and improve the mining performance. Experiments on sparse, very sparse, dense, and very dense datasets indicate that the proposed ECoHUPM algorithm is efficient as compared to the state-of-the-art CoHUIM and CoHUI-Miner algorithms in terms of both time and memory consumption.

Market basket analysis is one of the techniques of knowledge mining used in a broad dataset or database to find a collection of items that are interwoven. Generally used in a sale, the most relevant shopping cart data is used. This methodology has been widely applied in different multinational or foreign industries and is very useful in consumer buying preferences. Technology advances change business trends dramatically, shifting customer demands require increased surgical accuracy of business. In this research, the writer wants to analyze the shopping cart using apriori algorithm, with a dataset from the Kaggle web. Using anaconda software features with the Python programming language is expected to create knowledge overwriting consumer buying patterns. In conclusion, this pattern can be used to support industry in managing its company activities.

Salah satu teknik data mining yang populer digunakan adalah association data mining atau yang biasa disebut dengan istilah market basket analysis. Market basket didefinisikan sebagai suatu itemset yang dibeli secara bersamaan oleh pelanggan dalam suatu transaksi. Market basket analysis adalah suatu sarana untuk meningkatkan penjualan. Metode ini dimulai dengan mencari sejumlah frequent itemset dan dilanjutkan dengan pembentukan aturan-aturan asosiasi. Algoritma Apriori dan frequent pattern growth adalah dua algoritma yang sangat populer untuk menemukan sejumlah frequent itemset dari data-data transaksi yang tersimpan dalam basis data. Dalam penelitian ini algoritma frequent pattern growth (FP Growth) digunakan untuk menemukan sejumlah aturan asosiasi dari basis data transaksi penjualan di Swalayan KSU Sumber Makmur (Trenggalek). Dari hasil pengolahan data didapatkan pola pembelian paling kuat berupa jika membeli pasta gigi maka dimungkinkan juga akan membeli sabun dan jika membeli shampo juga akan membeli sabun dengan tingkat keyakinan (confidence) 63% dan 62%.

HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the HAUIM (High average-utility itemsets mining) where average-utility measure is used to obtain the utility of itemsets. HAUIM is the refined version of FIM (Frequent itemset mining) problem and has various applications in the field of market basket analysis, bio-informatics, text mining, network traffic analysis, product recommendation and e-learning among others. In this paper, we provide a comprehensive survey of the state-of-the-art methods of HAUIM to mine the HAUIs (High average-utility itemsets) from the static and dynamic datasets since the induction of the HAUIM problem. We discuss the pros and cons of each category of mining approaches in detail. The taxonomy of HAUIM is presented according to the mining approaches. Finally,various extensions, future directions and research opportunities of HAUIM algorithms are discussed.

Businesses overcome with a high degree of competition that necessitates customer-focused strategies in most industries. In a digitalized business environment, the implementation of such strategies often requires the analysis of customer data. Market basket analysis is a well-known method in marketing that examines basket data to discover useful information about customers’ purchase intentions. The analysis has been a playground for data mining researchers that aim to overcome with its practical challenges. Our study extends the conventional basket analysis by incorporating demographic variables along with purchase transactions. With such modification, we provide an example for the extraction of segment-specific rules that relate product-level purchase decisions with gender, location, and age group. For this purpose, we present a case study on monthly basket data obtained from an e-retailer in Turkey. Our findings demonstrate association rules that might guide marketing practitioners who need to discover segment-specific purchase patterns to designate personalized promotions.

The current study refers to the affinity analysis of a prescription and diagnosis dataset and focuses on how prescription behavior changes after the detection of a disorder. Using a subset of patients that have been diagnosed, at least once, with hypertension and hyperlipidemia, examines the prescription behavior of the doctor after the detection of gastroesophageal reflux (K21), or insulin-dependent diabetes mellitus (E10). Diagnosis and prescription data were collected during consecutive visits of 4473 patients in a 3 years period. The analysis of the prescription data before and after the diagnosis of K21 and E10 reveals the popular substances for each disorder, such as Metformin for E10, or Omeprazole for K21 and substances that are discontinued including the same popular substances as well. It also reveals that substances that treat the main disorders of the group, such as Simvastatin and Hydrochlorothiazide are discontinued after the diagnosis of K21 and E10. Apart from the medical findings, which must be subject of further research, the proposed methodology can be employed for studying the prescription behavior of physicians and how it changes after specific diagnoses.

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.

Purpose This study aims to apply association rule mining (ARM) to uncover specific associations between operating components of a chiller system and improve its coefficient of performance (COP), hence reducing the electricity use of buildings with central air conditioning. Design/methodology/approach First, 13 operating variables were identified, comprising measures of temperatures and flow rates of system components and their switching statuses. The variables were grouped into four bins before carrying out ARM. Strong rules were produced to associate the variables and switching statuses with different COP classes. Findings The strong rules explain existing constraints on practising chiller sequencing and prioritise variables for optimisation. Based on strong rules for the highest COP class, the optimal operating strategy involves rescheduling chillers and their associated components in pairs during a high load operation. Resetting the chilled water supply temperature is the next best strategy, followed by resetting the condenser water entering temperature, subject to operating constraints. Research limitations/implications This study considers the even frequency method with four bins only. Replication work can be done with other discretisation methods and different numbers of classes to compare potential differences in the bin ranges of the optimised variables. Practical implications The strong rules identified by ARM highlight associations between variables and high or low COPs. This supports the selection of critical variables and the operating status of system components to maximise the COP. Tailor-made optimisation strategies and the associated electricity savings can be further evaluated. Originality/value Previous studies applied ARM for chiller fault detection but without considering system performance under the interaction of different components. The novelty of this study is its demonstration of ARM’s intelligence at discovering associations in past operating data. This enables the identification of tailor-made energy management opportunities, which are essential for all engineering systems. ARM is free from the prediction errors of typical regression and black-box models.

Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.

For retail companies such as Gramedia stores, promotion and strategies to sell books are important, so tools are needed to analyze past sales data. Gramedia does not yet have tools to analyze shopping cart patterns that aim to carry out product promotions appropriately. To promote what books should be promoted using the market basket analysis method or shopping basket analysis. The algorithm used in the data mining process is Frequent Pattern Growth (FP Growth) because it is faster in processing large data. The data analyzed is historical data on book sales from January to March 2020 which is taken randomly (random sampling). The framework used in the data mining process is the Cross Industry Standard Process for Data Mining (CRISP-DM) and the tool used is the Rapid Miner using a market basket analysis framework. With a minimum support of 0.003 and a minimum confidence 0.3 using the FP-Growth algorithm to produce an item set of 7 rules to recommend product promotions. The algorithm results are also in accordance with the business understanding phase of CRISP-DM.

Modern IT systems collect detailed data on each activity, transaction, forum entry, conversation and many other areas. The availability of large data volumes in the business, industry and research fields opens up new opportunities for the empirical verification of various economic theories and laws. The analysis of big datasets in turn allows us to look at many issues from a new point of view and see the dependencies that are otherwise difficult to derive. In this paper, we propose a new measure for dependencies between goods in market basket data. The introduced measure was inspired by the well-known microeconomic concept of complementarity. Due to its similar properties to those of complementarity, the new measure was called basket complementarity (b-complementarity). B-complementarity not only measures the strength of dependencies between goods but also measures the direction of these dependencies. The values of the proposed measure can be relatively easily calculated using market basket data. This paper also presents a simple example illustrating this new concept, areas of possible application (e.g., in e-commerce) and preliminary results of searching for goods that meet the criteria of basket complementarity in real market basket data.

Pattern mining is an unsupervised data mining approach aims to find interesting patterns that can be used to support decision-making. High Utility Pattern Mining (HUPM) aims to extract patterns having high utility or importance which has broad applications in domains such as market basket analysis, product recommendation, bioinformatics, e-learning, text mining, and web click stream analysis. However, it has several limitations on real life scenarios; as a consequence, many extensions of HUPM appeared in the literature such as Correlated High Pattern Mining, Incremental Utility Mining, On-Shelf High Utility Pattern Mining, and Concise Representations of High Utility Patterns. The Correlated High Utility Pattern Mining aims to extract interesting high utility patterns by utilizing both Utility and Correlation measures. Several algorithms have been proposed to mine the correlated high utility patterns. These algorithms differ in the measures used to evaluate the interestingness of the patterns, data structures and pruning properties which they use to improve the mining performance. This paper presents a detailed survey on correlated high utility pattern mining, their methods, measures, data structures and pruning properties.

Data sanitization process is employed to market the sharing of transactional databases among organizations and businesses, and alleviates concerns for people and organizations regarding the disclosure of sensitive patterns sanitization process converts the source database into a released database so that unauthorized person cannot identify the sensitive patterns and so data confidentiality is maintained using association rule mining method. This process strongly relies on the minimizing the impact of knowledge sanitization on the info utility by minimizing the amount of lost patterns within the sort of non– sensitive patterns which are not mined from sanitized database. This study proposes a knowledge sanitization algorithm to cover sensitive patterns within the sort of frequent item sets from the database while controlling the impact of sanitization on the data utility using estimation of impact factor of every modification on non-sensitive item sets. In some applications like market basket analysis, Association Rule Mining (ARM) has recently gained more attention in businesses where the regularities within the customer purchasing behavior are found. On the other hand, these discovered patterns may pose a threat to the privacy of data holder; therefore, these patterns should be hidden before data sharing in such a way that the adversaries cannot discover the regularities in customer purchasing behavior.

With the rapid development in business transactions, especially in recent years, it has become necessary to develop different mechanisms to trace business user records in web server log in an efficient way. Online business transactions have increased, especially when the user or customer cannot obtain the required service. For example, with the spread of the epidemic Coronavirus (COVID-19) throughout the world, there is a dire need to rely more on online business processes. In order to improve the efficiency and performance of E-business structure, a web server log must be well utilized to have the ability to trace and record infinite user transactions. This paper proposes an event stream mechanism based on formula patterns to enhance business processes and record all user activities in a structured log file. Each user activity is recorded with a set of tracing parameters that can predict the behavior of the user in business operations. The experimental results are conducted by applying clustering-based classification algorithms on two different datasets;namely, Online Shoppers Purchasing Intention and Instacart Market Basket Analysis. The clustering process is used to group related objects into the same cluster, then the classification process measures the predicted classes of clustered objects. The experimental results record provable accuracy in predicting user preferences on both datasets. © 2021 Tech Science Press. All rights reserved.

Complex systems can be characterized by their level of order or disorder. An ordered system is related to the presence of system properties that are correlated with each other. For example, it has been found in crisis periods that the financial systems tend to be synchronized, and symmetry appears in financial assets' behavior. In retail, the collective purchasing behavior tends to be highly disorderly, with a diversity of correlation patterns appearing between the available market supply. In those cases, it is essential to understand the hierarchical structures underlying these systems. For the latter, community detection techniques have been developed to find similar behavior clusters according to some similarity measure. However, these techniques do not consider the inherent interactions between the multitude of system elements. This paper proposes and tests an approach that incorporates a hierarchical grouping process capable of dealing with complete weighted networks. Experiments show that the proposal is superior in terms of the ability to find minimal energy clusters. These minimum energy clusters are equivalent to system states (market baskets) with a higher probability of occurrence; therefore, they are interesting for marketing and promotion activities in retail environments.

An unsupervised machine learning model of association rule known as market basket analysis is proposed in this study to analyze the influence of various socio-economic factors on the choice of the water source. Data of 51 socio-economic factors collected from 295 individuals living in 65 households in Ambo city in the Oromia region of Ethiopians were used for this purpose. The results revealed (i) 64% of the family preferred multiple water sources (i.e., public tap and river water), (ii) the water was collected females in 92% of the households, and (iii) majority of people preferred bathing and laundering in the river (support = 32% and confidence = 87%). Direct utilization of river water is not a preferable choice for the user since it may lead to severe health issues and cause water pollution from bathing and laundering. Education and monthly income have a significant impact on the choices of water sources. Local management authorities can improve sanitation and public health management using the results obtained in the study. The paper only gives a glimpse of the important factors that should be considered for improving the way of life for the underdeveloped areas of the world using advanced machine learning techniques.

The purpose of this paper is to answer the question whether, despite the differentiation of the corporate income tax in the European Union, there are similarity patterns allowing for the harmonization of the bases of this tax. The analysed CIT static data both quantitative and qualitative concerns the years 2018 and 2020. The method of hierarchical cluster analysis allowed a grouping of EU countries according to their similarities. It also indicated the greatest tax differences between EU member states. In turn the affinity analysis made it possible to distinguish groups of countries which are similar in terms of CIT with the simultaneous identification of a pattern. Results show that despite significant differences in tax rates some EU states show convergence in tax bases. The geographical criterion still plays an important role in determining CIT structure and tax incentives are one of the tools that may be used in the approximation of the bases.

We analyze market baskets of individual households in two consumer durables categories (music, computer related products) by the multivariate logit (MVL) model, its finite mixture extension (FM-MVL) and the conditional restricted Boltzmann machine (CRBM). The CRBM attains a vastly better out-of-sample performance than MVL and FM-MVL models. Based on simulation-based likelihood ratio tests we prefer the CRBM to the FM-MVL model. To interpret hidden variables of conditional Boltzmann machines we look at their average probability differences between purchase and non-purchases of any sub-category across all baskets. To measure interdependences we compute cross effects between sub-categories for the best performing FM-MVL model and CRBM. In both product categories the CRBM indicates more or higher positive cross effects than the FM-MVL model. Finally, we suggest appropriate future research based on larger and more detailed data sets.

Tujuan penelitian ini adalah untuk mengetahui dan menganalisis pola pembelian produk pada e-commerce Panenmart. Metode dasar yang digunakan adalah metode eksperimen. Teknik pengambilan sampel pada penelitian ini adalah sampling jenuh (sensus). Data yang digunakan dalam penelitian bersumber dari data sekunder yang diperoleh dari data transaksi belanja yang didapatkan melalui database transaksi penjualan website dan aplikasi mobile Panenmart. Untuk mengetahui pola aturan asosiasi pembelian produk maka digunakan Market Basket Analysis (MBA) berdasarkan tiga metrik, yaitu support, confidence dan lift ratio. Pada penelitian ini menggunakan nilai minimum support 0,1 dan nilai minimum confidence 0,3. Hasil penelitian menunjukkan 3 pola aturan asosiasi pembelian produk yang memiliki hubungan yang kuat dan valid dibeli secara bersamaan karena memiliki nilai confidence lebih dari 0,3 dan nilai lift ratio > 1. Adapun itemset produk yang memiliki pola aturan yaitu, cabe rawit dan buncis; jagung dan bayam; bayam dan buncis.

Encouraged by multiple federal policies, healthcare organizations are assuming greater responsibility for patients' social needs. This study describes the individual and co-occurring social needs t...