Dataset

This dataset was created by Shashank

Contents

The data are provided to illustrate methods in evaluating systematic transactional data reuse in machine learning. A library account-based recommender system was developed using machine learning processing over transactional data of 383,828 transactions (or check-outs) sourced from a large multi-unit research library. The machine learning process utilized the FP-growth algorithm over the subject metadata associated with physical items that were checked-out together in the library. The purpose of this research is to evaluate the results of systematic transactional data reuse in machine learning. The analysis herein contains a large-scale network visualization of 180,441 subject association rules and corresponding node metrics.

Within the confines of this document, we embark on a comprehensive journey delving into the intricacies of a dataset meticulously curated for the purpose of association rules mining. This sophisticated data mining technique is a linchpin in the realms of market basket analysis. The dataset in question boasts an array of items commonly found in retail transactions, each meticulously encoded as a binary variable, with "1" denoting presence and "0" indicating absence in individual transactions.

Our dataset unfolds as an opulent tapestry of distinct columns, each dedicated to the representation of a specific item:

• Bread
• Honey
• Bacon
• Toothpaste
• Banana
• Apple
• Hazelnut
• Cheese
• Meat
• Carrot
• Cucumber
• Onion
• Milk
• Butter
• ShavingFoam
• Salt
• Flour
• HeavyCream
• Egg
• Olive
• Shampoo
• Sugar

The raison d'être of this dataset is to serve as a catalyst for the discovery of intricate associations and patterns concealed within the labyrinthine network of customer transactions. Each row in this dataset mirrors a solitary transaction, while the values within each column serve as sentinels, indicating whether a particular item was welcomed into a transaction's embrace or relegated to the periphery.

The data within this repository is rendered in a binary symphony, where the enigmatic "1" enunciates the acquisition of an item, and the stoic "0" signifies its conspicuous absence. This binary manifestation serves to distill the essence of the dataset, centering the focus on item presence, rather than the quantum thereof.

This dataset unfurls its wings to encompass an assortment of prospective applications, including but not limited to:

• Market Basket Analysis: Discerning items that waltz together in shopping carts, thus bestowing enlightenment upon the orchestration of product placement and marketing strategies.
• Recommender Systems: Crafting bespoke product recommendations, meticulously tailored to each customer's historical transactional symphony.
• Inventory Management: Masterfully fine-tuning stock levels for items that find kinship in frequent co-acquisition, thereby orchestrating a harmonious reduction in carrying costs and stockouts.
• Customer Behavior Analysis: Peering into the depths of customer proclivities and purchase patterns, paving the way for the sculpting of exquisite marketing campaigns.

The treasure trove of this dataset beckons the deployment of quintessential techniques, among them the venerable Apriori and FP-Growth algorithms. These stalwart algorithms are proficient at ferreting out the elusive frequent itemsets and invaluable association rules, shedding light on the arcane symphony of customer behavior and item co-occurrence patterns.

In closing, the association rules dataset unfurled before you offers an alluring odyssey, replete with the promise of discovering priceless patterns and affiliations concealed within the tapestry of transactional data. Through the artistry of data mining algorithms, businesses and analysts stand poised to unearth hitherto latent insights capable of steering the helm of strategic decisions, elevating the pantheon of customer experiences, and orchestrating the symphony of operational optimization.

The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

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Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

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Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

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To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

Frequent subgraph mining, i.e., the identification of relevant patterns in graph databases, is a well-known data mining problem with high practical relevance, since next to summarizing the data, the resulting patterns can also be used to define powerful domain-specific similarity functions for prediction. In recent years, significant progress has been made towards subgraph mining algorithms that scale to complex graphs by focusing on tree patterns and probabilistically allowing a small amount of incompleteness in the result. Nonetheless, the complexity of the pattern matching component used for deciding subtree isomorphism on arbitrary graphs has significantly limited the scalability of existing approaches. In this paper, we adapt sampling techniques from mathematical combinatorics to the problem of probabilistic subtree mining in arbitrary databases of many small to medium-size graphs or a single large graph. By restricting on tree patterns, we provide an algorithm that approximately counts or decides subtree isomorphism for arbitrary transaction graphs in sub-linear time with one-sided error. Our empirical evaluation on a range of benchmark graph datasets shows that the novel algorithm substantially outperforms state-of-the-art approaches both in the task of approximate counting of embeddings in single large graphs and in probabilistic frequent subtree mining in large databases of small to medium sized graphs.

Overview

The Market_Basket_Optimisation dataset is a classic transactional dataset often used in association rule mining and market basket analysis.
It consists of multiple transactions where each transaction represents the collection of items purchased together by a customer in a single shopping trip.

• File Name: Market_Basket_Optimisation.csv
• Format: CSV (Comma-Separated Values)
• Structure: Each row corresponds to one shopping basket. Each column in that row contains an item purchased in that basket.
• Nature of Data: Transactional, categorical, sparse.
• Primary Use Case: Discovering frequent itemsets and association rules to understand shopping patterns, product affinities, and to build recommender systems.

Detailed Information

📊 Dataset Composition

• Transactions: 7,501 (each row = one basket).
• Items (unique): Around 120 distinct products (e.g., bread, mineral water, chocolate, etc.).
• Columns per row: Up to 20 possible items (not fixed; some rows have fewer, some more).
• Data Type: Purely categorical (no numerical or continuous features).
• Missing Values: Present in the form of empty cells (since not every basket has all 20 columns).
• Duplicates: Some baskets may appear more than once — this is acceptable in transactional data as multiple customers can buy the same set of items.

🛒 Nature of Transactions

• Basket Definition: Each row captures items bought together during a single visit to the store.
• Variability: Basket size varies from 1 to 20 items. Some customers buy only one product, while others purchase a full set of groceries.
• Sparsity: Since there are ~120 unique items but only a handful appear in each basket, the dataset is sparse. Most entries in the one-hot encoded representation are zeros.

🔎 Examples of Data

Example transaction rows (simplified):

Here, empty cells mean no item was purchased in that slot.

📈 Applications of This Dataset

This dataset is frequently used in data mining, analytics, and recommendation systems. Common applications include:

1. Association Rule Mining (Apriori, FP-Growth):

   • Discover rules like {Bread, Butter} ⇒ {Jam} with high support and confidence.
   • Identify cross-selling opportunities.

2. Product Affinity Analysis:

   • Understand which items tend to be purchased together.
   • Helps with store layout decisions (placing related items near each other).

3. Recommendation Engines:

   • Build systems that suggest "You may also like" products.
   • Example: If a customer buys pasta and tomato sauce, recommend cheese.

4. Marketing Campaigns:

   • Bundle promotions and discounts on frequently co-purchased products.
   • Personalized offers based on buying history.

5. Inventory Management:

   • Anticipate demand for certain product combinations.
   • Prevent stockouts of items that drive the purchase of others.

📌 Key Insights Potentially Hidden in the Dataset

• Popular Items: Some items (like mineral water, eggs, spaghetti) occur far more frequently than others.
• Product Pairs: Frequent pairs and triplets (e.g., pasta + sauce + cheese) reflect natural meal-prep combinations.
• Basket Size Distribution: Most customers buy fewer than 5 items, but a small fraction buy 10+ items, showing long-tail behavior.
• Seasonality (if extended with timestamps): Certain items might show peaks in demand during weekends or holidays (though timestamps are not included in this dataset).

📂 Dataset Limitations

1. No Customer Identifiers:

   • We cannot track repeated purchases by the same customer.
   • Analysis is limited to basket-level insights.

2. No Timestamps:

   • No temporal analysis (trends over time, seasonality) is possible.

3. No Quantities or Prices:

   • We only know whether an item was purchased, not how many units or its cost.

4. Sparse & Noisy:

   • Many baskets are small (1–2 items), which may produce weak or trivial rules.

🔮 Potential Extensions

• Synthetic Timestamps: Assign simulated timestamps to study temporal buying patterns.
• Add Customer IDs: If merged with external data, one can perform personalized recommendations.
• Price Data: Adding cost allows for profit-driven association rules (not just frequency-based).
• Deep Learning Models: Sequence models (RNNs, Transformers) could be applied if temporal ordering of items is introduced.

...

Intelligent Pallet Pattern Teaching via AR Market Outlook

According to our latest research, the global Intelligent Pallet Pattern Teaching via AR market size reached USD 485 million in 2024. The market is expected to grow at a robust CAGR of 18.3% during the forecast period, reaching an estimated USD 2,335 million by 2033. This impressive growth trajectory is driven primarily by the increasing adoption of automation and digital transformation initiatives across logistics, manufacturing, and retail industries. The integration of augmented reality (AR) into pallet pattern teaching is revolutionizing traditional warehouse and logistics operations, enabling organizations to achieve higher accuracy, efficiency, and cost savings.

One of the primary growth factors propelling the Intelligent Pallet Pattern Teaching via AR market is the rapid digitalization of supply chain and warehouse management processes. Companies are increasingly seeking innovative solutions to optimize palletization, reduce human error, and enhance overall productivity. The deployment of AR-based systems for pallet pattern teaching allows for real-time visualization, step-by-step guidance, and instant corrections, which significantly improve the accuracy and speed of pallet loading. This technology is particularly valuable in environments with high product variability or complex packing requirements, where traditional training and manual pattern recognition fall short. As industries continue to face mounting pressure to deliver faster and more accurate order fulfillment, the demand for intelligent AR-enabled solutions is expected to surge.

Another significant driver for the market is the ongoing labor shortages and rising labor costs in the logistics and manufacturing sectors. The implementation of intelligent pallet pattern teaching via AR not only reduces the dependency on highly skilled labor but also accelerates the onboarding process for new employees. By providing intuitive, hands-on training through AR devices, organizations can minimize training time, lower operational risks, and ensure consistent adherence to best practices. Furthermore, the data-driven insights generated by these systems enable continuous process improvement and facilitate compliance with safety and quality standards. The convergence of AR technology with artificial intelligence (AI) and machine learning (ML) further enhances the capabilities of these solutions, enabling predictive analytics and adaptive learning for evolving operational challenges.

The increasing focus on sustainability and cost optimization is also fueling the adoption of intelligent pallet pattern teaching via AR. Inefficient palletization leads to suboptimal use of space, increased transportation costs, and higher carbon emissions due to more frequent shipments. By leveraging AR-guided pallet pattern teaching, companies can maximize load density, minimize product damage, and reduce overall logistics costs. This not only contributes to better financial outcomes but also aligns with global sustainability goals by lowering the environmental footprint of supply chain operations. As regulatory frameworks and customer expectations around sustainability continue to tighten, the role of intelligent AR solutions in supporting green logistics will become even more pronounced.

From a regional perspective, North America currently leads the Intelligent Pallet Pattern Teaching via AR market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific region. The strong presence of advanced logistics infrastructure, early adoption of AR technologies, and a high concentration of leading technology providers have positioned North America at the forefront of market growth. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by rapid industrialization, expanding e-commerce, and significant investments in smart manufacturing and logistics automation. Europe maintains a steady growth trajectory, supported by stringent regulatory standards and a strong focus on operational efficiency. Latin America and the Middle East & Africa are also witnessing increasing adoption, albeit at a slower pace, as companies in these regions begin to recognize the value proposition of AR-enabled pallet pattern teaching.

Component Analysis

The Intelligent Pallet Pattern Teaching via AR market is segmented by component into hardware, software, and services, each playing a critical role in the deployment and operation of AR-enabled palletization solut

Comprehensive YouTube channel statistics for Electrónica FP, featuring 446,000 subscribers and 48,529,037 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in ES. Track 422 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

This is the main data used in my paper "Financial Development, Economic Growth, and Institutional Features: Is There a Common Pattern?".

Comprehensive YouTube channel statistics for Technical FP, featuring 826,000 subscribers and 110,745,254 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Technology category and is based in IN. Track 778 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

The long-standing view that biomass growth in trees typically follows a rise-and-fall unimodal pattern has been challenged by studies concluding that biomass growth increases with size even among the largest stems in both closed forests and in open competition-free environments. We highlight challenges and pitfalls that influence such interpretations. The ability to observe and calibrate biomass change in large stems requires adequate data regarding these specific stems. Data checking and control procedures can bias estimates of biomass growth and generate false increases with stem size. It is important to distinguish aggregate and individual-level trends: a failure to do so results in flawed interpretations. Our assessment of biomass growth in 706 tropical forest stems indicates that individual biomass growth patterns often plateau for extended periods, with no significant difference in the number of stems indicating positive and negative trends in all but one of the 14 species. Nonetheless, when comparing aggregate growth during the most recent five years, 13 out of our 14 species indicate that biomass growth increases with size even among the largest sizes. Thus, individual and aggregate patterns of biomass growth with size are distinct. Claims concerning general biomass growth patterns for large trees remain unconvincing. We suggest how future studies can improve our knowledge of growth patterns in and among large trees.

Collection of environmental datasets recorded with Tinkerforge sensors and used in the development of a bachelor thesis on the topic of frequent pattern mining. The data was collected in several locations in the city of Graz, Austria, as well as an additional dataset recorded in Santander, Spain. The following bricklets were used:

Graz datasets (i12, library_at, mensa_nt, muenzgrabenstrasse, neutorgasse, studienzentrum, vguh, kaiserfeldgasse):

Barometer Bricklet

Moisture Bricklet

Sound Intensity Bricklet

Ambient Light Bricklet

Humidity Bricklet

Temperature Bricklet

CO2 Bricklet

Motion Detector Bricklet

Barometer Bricklet

Santander dataset:

Motion Detector Bricklet

Ambient Light Bricklet 2.0

Sound Intensity Bricklet

Temperature Bricklet

Humidity Bricklet

CO2 Bricklet

Accelerometer Bricklet

Barometer Bricklet (recording also altitude)

Additionally, the datasets contain the voltage and chip temperature readings of the Master Brick.

It should be noted that Tinkerforge bricklets occasionally do not manage to write their recorded values in the time window between two recording frames, and they can also suffer from other disruptions. This produces a considerable number of instances that do not include the data of all sensors (incomplete instants), as well as some readings flagged as erroneous, which should be taken into account when working with the datasets.

Base de datos actualizada de licitaciones públicas con código CPV 72212940 (Pattern design and calendar software development services). Estadísticas, gráficos y licitaciones actualizadas a Noviembre de 2025.

BackgroundConcerns about the role of chronically used medications in the clinical outcomes of the coronavirus disease 2019 (COVID-19) have remarkable potential for the breakdown of non-communicable diseases (NCDs) management by imposing ambivalence toward medication continuation. This study aimed to investigate the association of single or combinations of chronically used medications in NCDs with clinical outcomes of COVID-19.MethodsThis retrospective study was conducted on the intersection of two databases, the Iranian COVID-19 registry and Iran Health Insurance Organization. The primary outcome was death due to COVID-19 hospitalization, and secondary outcomes included length of hospital stay, Intensive Care Unit (ICU) admission, and ventilation therapy. The Anatomical Therapeutic Chemical (ATC) classification system was used for medication grouping. The frequent pattern growth algorithm was utilized to investigate the effect of medication combinations on COVID-19 outcomes.FindingsAspirin with chronic use in 10.8% of hospitalized COVID-19 patients was the most frequently used medication, followed by Atorvastatin (9.2%) and Losartan (8.0%). Adrenergics in combination with corticosteroids inhalants (ACIs) with an odds ratio (OR) of 0.79 (95% confidence interval: 0.68–0.92) were the most associated medications with less chance of ventilation therapy. Oxicams had the least OR of 0.80 (0.73–0.87) for COVID-19 death, followed by ACIs [0.85 (0.77–0.95)] and Biguanides [0.86 (0.82–0.91)].ConclusionThe chronic use of most frequently used medications for NCDs management was not associated with poor COVID-19 outcomes. Thus, when indicated, physicians need to discourage patients with NCDs from discontinuing their medications for fear of possible adverse effects on COVID-19 prognosis.

IntroductionChildren’s well-being is highly influenced by their fetal growth. Adequate intrauterine growth (IUG) is a basic feature of a healthy pregnancy. The aim of our study was to assess IUG patterns in a rural and drought-affected population in the Rift Valley area of the Adami Tullu district in Oromia, Ethiopia.MethodsWe conducted a longitudinal, community-based study of IUG patterns utilizing serial ultrasound measurements. Data were collected for 17 months, from July 2016 to November 2017. We included 675 singleton foetuses ≤ 24 weeks old, based on ultrasound-derived estimates of gestational age, and followed them until delivery. We obtained head circumference, biparietal diameter, abdominal circumference, femur length, and estimated fetal weight at 26, 30, and 36 weeks. Fetal weight was estimated using the Hadlock algorithm, and the 5th, 10th, 25th, 50th, 75th, 90th, and 95th centiles were developed from this model. We compared the biometric measurements and fetal weight data from our study to the World Health Organization (WHO) and INTERGROWTH-21st fetal growth reference standards.ResultsDistribution of the biometric measurements and estimated fetal weights in our study were similar to those for the WHO and INTERGROWTH-21st references. Most measurements were between -2 and +2 of the reference z-scores. Based on the smoothed percentiles, the 5th, 50th, and 95th percentiles of our study had similar distribution patterns to the WHO chart, and the 50th percentile had a similar pattern to the INTERGROWTH-21st chart.ConclusionsOur study determined fetal growth patterns in a drought-affected rural community of Ethiopia using common ultrasound biometric measurements. We found similar IUG patterns to those indicated in the WHO and INTERGROWTH-21st fetal growth reference standards.

Tomato is one of the most appreciated vegetables in the world. Predicting its yield and optimizing its culture is important for global food security. This paper addresses the challenge of finding optimum climatic values for a high tomato yield. The Frequent Pattern Growth (FPG) algorithm was considered to establish the associations between six climate variables: minimum and maximum temperatures, maximum humidity, sunshine (Sun), rainfall, and evapotranspiration (ET), collected over 26 years in the three agro-ecological Zones of Benin. Monthly climate data were aggregated with yield data over the same period. After aggregation, the data were transformed into ‘low’, ‘medium’, and ‘high’ attributes using the threshold values defined. Then, the rules were generated using the minimum support set to 0.2 and the confidence to 0.8. Only the rules with the consequence ‘high yield’ were screened. The best yield patterns were observed in the Guinean Zone, followed by the Sudanian. The results indicated that high tomato yield was associated with low ET in all areas considered. Minimum and maximum temperatures, maximum humidity, and Sun were medium in every Zone. Moreover, rainfall was high in the Sudanian Zone, unlike the other regions where it remained medium. These results are useful in assessing climate variability’s impact on tomato production. Thus, they can help farmers make informed decisions on cultivation practices to optimize production in a changing environment. In addition, the findings of this study can be considered in other regions and adapted to other crops.

Threshold values of variables for Sudano-Guinean Zone.

Tomato is one of the most appreciated vegetables in the world. Predicting its yield and optimizing its culture is important for global food security. This paper addresses the challenge of finding optimum climatic values for a high tomato yield. The Frequent Pattern Growth (FPG) algorithm was considered to establish the associations between six climate variables: minimum and maximum temperatures, maximum humidity, sunshine (Sun), rainfall, and evapotranspiration (ET), collected over 26 years in the three agro-ecological Zones of Benin. Monthly climate data were aggregated with yield data over the same period. After aggregation, the data were transformed into ‘low’, ‘medium’, and ‘high’ attributes using the threshold values defined. Then, the rules were generated using the minimum support set to 0.2 and the confidence to 0.8. Only the rules with the consequence ‘high yield’ were screened. The best yield patterns were observed in the Guinean Zone, followed by the Sudanian. The results indicated that high tomato yield was associated with low ET in all areas considered. Minimum and maximum temperatures, maximum humidity, and Sun were medium in every Zone. Moreover, rainfall was high in the Sudanian Zone, unlike the other regions where it remained medium. These results are useful in assessing climate variability’s impact on tomato production. Thus, they can help farmers make informed decisions on cultivation practices to optimize production in a changing environment. In addition, the findings of this study can be considered in other regions and adapted to other crops.

Threshold values of variables for the Guinean Zone.