In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell’s concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744−0.792) and 0.745 (95% CI, 0.669−0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30–54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.

Analysis of ‘Groceries dataset ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/heeraldedhia/groceries-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Association Rule Mining

Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy.

Association Rules are widely used to analyze retail basket or transaction data and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules.

Details of the dataset

The dataset has 38765 rows of the purchase orders of people from the grocery stores. These orders can be analysed and association rules can be generated using Market Basket Analysis by algorithms like Apriori Algorithm.

Apriori Algorithm

Apriori is an algorithm for frequent itemset mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent itemsets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.

An example of Association Rules

Assume there are 100 customers 10 of them bought milk, 8 bought butter and 6 bought both of them. bought milk => bought butter support = P(Milk & Butter) = 6/100 = 0.06 confidence = support/P(Butter) = 0.06/0.08 = 0.75 lift = confidence/P(Milk) = 0.75/0.10 = 7.5

Note: this example is extremely small. 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.

Some important terms:

• Support: This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears.

• Confidence: This says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}. This is measured by the proportion of transactions with item X, in which item Y also appears.

• Lift: This says how likely item Y is purchased when item X is purchased while controlling for how popular item Y is.

--- Original source retains full ownership of the source dataset ---

Association rule is an important technique in data mining.

Introduction

This dataset is designed for Market Basket Analysis, specifically focusing on Itemset Mining. It is inspired by datasets commonly used to demonstrate the Apriori algorithm, a fundamental technique for discovering frequent itemsets in transactional data.

The Apriori algorithm, proposed by Agrawal and Srikant in 1994, operates on databases containing transactions to uncover relationships between items frequently purchased together. It forms the basis of Association Rule Mining, a crucial method used in recommendation systems, sales optimization, and customer behavior analysis.

This dataset is particularly useful for students and researchers exploring data mining techniques, with a specific application in the CS313 course at the University of Information Technology (UIT) under the topic "Mining Frequent Patterns: Application of Itemset Mining."

Dataset Structure

The dataset consists of transactional data represented in a binary format.

• Columns: Each column corresponds to a specific product, including:
  Apple, Bread, Butter, Cheese, Corn, Dill, Eggs, Ice Cream, Kidney Beans, Milk, Nutmeg, Garlic, Sugar, Broccoli, Yogurt, Chocolate.
• Rows: Each row represents a transaction, with values:
  • 'True' – the item is present in the transaction.
  • 'False' – the item is not included in the transaction.

This dataset is well-suited for applying Apriori, FP-Growth, or other frequent itemset mining techniques, making it valuable for real-world applications such as product recommendations, inventory optimization, and customer purchasing behavior analysis.

Source

This dataset is inspired by publicly available datasets used to demonstrate the Apriori algorithm. A similar dataset can be found on Kaggle, which serves as a reference for frequent itemset mining studies:

Kaggle - Datasets for Apriori.

While this dataset is tailored for the CS313 course at UIT, it follows the general structure of transaction-based datasets commonly used in Market Basket Analysis research and coursework.

Additional file 2. The integral table of transactions T.

Association Rule Mining

Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy.

Association Rules are widely used to analyze retail basket or transaction data and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules.

Details of the dataset

The dataset has 38765 rows of the purchase orders of people from the grocery stores. These orders can be analysed and association rules can be generated using Market Basket Analysis by algorithms like Apriori Algorithm.

Apriori Algorithm

Apriori is an algorithm for frequent itemset mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent itemsets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.

An example of Association Rules

Assume there are 100 customers 10 of them bought milk, 8 bought butter and 6 bought both of them. bought milk => bought butter support = P(Milk & Butter) = 6/100 = 0.06 confidence = support/P(Butter) = 0.06/0.08 = 0.75 lift = confidence/P(Milk) = 0.75/0.10 = 7.5

Note: this example is extremely small. 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.

Some important terms:

• Support: This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears.

• Confidence: This says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}. This is measured by the proportion of transactions with item X, in which item Y also appears.

• Lift: This says how likely item Y is purchased when item X is purchased while controlling for how popular item Y is.

Additional file 3. The integral matrix of concordance indices.

Analysis of ‘Groceries dataset for Market Basket Analysis(MBA)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rashikrahmanpritom/groceries-dataset-for-market-basket-analysismba on 13 November 2021.

--- Dataset description provided by original source is as follows ---

The initial dataset was collected from Groceries dataset. Then data was modified and fragmented into 2 datasets for ease of MBA implementation. Here the "groceries data.csv" contains groceries transaction data from which you can do EDA and pre-process the data to feed it in the apriori algorithm. But I have also added pre-processed data as "basket.csv" from which you'll just need to replace nan and encode it using TransactionEncoder after that you can feed the encoded data into the apriori algorithm.

--- Original source retains full ownership of the source dataset ---

Dataset of the paper: "Exploring the Usability of the Text-based CAPTCHA on Tablet Computers", by Darko Brodic and Alessia Amelio

The hyperparameters of the apriori algorithm.

From multiple raster datasets to spatial association patterns, the data-mining technique is divided into three subtasks, i.e., raster dataset pretreatment, mining algorithm design, and spatial pattern exploration from the mining results. Comparison with the former two subtasks reveals that the latter remains unresolved. Confronted with the interrelated marine environmental parameters, we propose a Tree-based Approach for eXploring Marine Spatial Patterns with multiple raster datasets called TAXMarSP, which includes two models. One is the Tree-based Cascading Organization Model (TCOM), and the other is the Spatial Neighborhood-based CAlculation Model (SNCAM). TCOM designs the “Spatial node→Pattern node” from top to bottom layers to store the table-formatted frequent patterns. Together with TCOM, SNCAM considers the spatial neighborhood contributions to calculate the pattern-matching degree between the specified marine parameters and the table-formatted frequent patterns and then explores the marine spatial patterns. Using the prevalent quantification Apriori algorithm and a real remote sensing dataset from January 1998 to December 2014, a successful application of TAXMarSP to marine spatial patterns in the Pacific Ocean is described, and the obtained marine spatial patterns present not only the well-known but also new patterns to Earth scientists.

Summarised information pertaining to (a) the number of samples, (b) the number of generated association rules (total as well as rules that involve 3 or more genera), (c) the unique number of microbial genera involved in the identified association rules, (d) execution time, and (e) the number of rules generated using an alternative rule mining strategy (detailed in discussion section of the manuscript).

A zip archive containing microbial abundance tables which were employed for deciphering association rules using the customised version of the Apriori algorithm. (ZIP)

Analysis of high-throughput omics data is one of the most important approaches for obtaining information regarding interactions between proteins/genes. Time-series omics data are a series of omics data points indexed in time order and normally contain more abundant information about the interactions between biological macromolecules than static omics data. In addition, phosphorylation is a key posttranslational modification (PTM) that is indicative of possible protein function changes in cellular processes. Analysis of time-series phosphoproteomic data should provide more meaningful information about protein interactions. However, although many algorithms, databases, and websites have been developed to analyze omics data, the tools dedicated to discovering molecular interactions from time-series omics data, especially from time-series phosphoproteomic data, are still scarce. Moreover, most reported tools ignore the lag between functional alterations and the corresponding changes in protein synthesis/PTM and are highly dependent on previous knowledge, resulting in high false-positive rates and difficulties in finding newly discovered protein–protein interactions (PPIs). Therefore, in the present study, we developed a new method to discover protein–protein interactions with the delayed comparison and Apriori algorithm (DCAA) to address the aforementioned problems. DCAA is based on the idea that there is a lag between functional alterations and the corresponding changes in protein synthesis/PTM. The Apriori algorithm was used to mine association rules from the relationships between items in a dataset and find PPIs based on time-series phosphoproteomic data. The advantage of DCAA is that it does not rely on previous knowledge and the PPI database. The analysis of actual time-series phosphoproteomic data showed that more than 68% of the protein interactions/regulatory relationships predicted by DCAA were accurate. As an analytical tool for PPIs that does not rely on a priori knowledge, DCAA should be useful to predict PPIs from time-series omics data, and this approach is not limited to phosphoproteomic data.

Background purposeThe adjunctive effect of acupuncture for cerebral infarction (CI) remains inconsistent. We aimed to determine its anti-inflammatory effect, assess safety, and summarize the adjunctive use of acupuncture for CI.MethodsWe identified qualified randomized controlled trials (RCTs) from eight literature databases. Frequency analysis and Apriori association analysis were conducted using SPSS Modeler 18.0 and SPSS 26.0 software. A meta-analysis was performed using Stata 17.0 software. The credibility of the meta-results and the certainty of the evidence was assessed using trial sequential analysis (TSA) and GRADE methods, respectively.ResultsA total of 43 RCTs were included, comprising 3,861 participants. Acupuncture with intermittent treatment (5–7 times per week), a combination of multiple points and multiple meridians (an average of 9.35 points in each prescription), typically lasting for 2–4 weeks, was commonly used for CI treatment. Meta-analysis indicated that the adjunctive use of acupuncture reduced levels of TNF-α (SMD = −1.36; 95% CI −1.51 to −1.20, p < 0.01), hs-CRP (SMD = −0.86; 95% CI −0.99 to −0.74, p < 0.01), and IL-6 (SMD = −0.85; 95% CI −1.08 to −0.62, p < 0.01), and decreased the rate of adverse events (RR = 0.71; 95% CI 0.49 to 1.01; p < 0.05). The certainty of the evidence was rated as moderate to high.ConclusionIntermittent acupuncture treatment lasting at least 2 w was commonly used for CI patients, typically involving multiple acupuncture points and meridians. Acupuncture demonstrated an anti-inflammatory effect in the treatment of CI. However, due to the low quality of the existing literature, high-quality randomized controlled trials (RCTs) are required to confirm these results in the future.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42017078583.

Purpose: To demonstrate an interaction-based method for the refinement of Gene Set Enrichment Analysis (GSEA) results.Method: Intravitreal injection of miR-124-3p antagomir was used to knockdown the expression of miR-124-3p in mouse retina at postnatal day 3 (P3). Whole retinal RNA was extracted for mRNA transcriptome sequencing at P9. After preprocessing the dataset, GSEA was performed, and the leading-edge subsets were obtained. The Apriori algorithm was used to identify the frequent genes or gene sets from the union of the leading-edge subsets. A new statistic d was introduced to evaluate the frequent genes or gene sets. Reverse transcription quantitative PCR (RT-qPCR) was performed to validate the expression trend of candidate genes after the knockdown of miR-124-3p.Results: A total of 115,140 assembled transcript sequences were obtained from the clean data. With GSEA, the NOD-like receptor signaling pathway, C-type-like lectin receptor signaling pathway, phagosome, necroptosis, JAK-STAT signaling pathway, Toll-like receptor signaling pathway, leukocyte transendothelial migration, chemokine signaling pathway, NF-kappa B signaling pathway and RIG-I-like signaling pathway were identified as the top 10 enriched pathways, and their leading-edge subsets were obtained. After being refined by the Apriori algorithm and sorted by the value of the modulus of d, Prkcd, Irf9, Stat3, Cxcl12, Stat1, Stat2, Isg15, Eif2ak2, Il6st, Pdgfra, Socs4 and Csf2ra had the significant number of interactions and the greatest value of d to downstream genes among all frequent transactions. Results of RT-qPCR validation for the expression of candidate genes after the knockdown of miR-124-3p showed a similar trend to the RNA-Seq results.Conclusion: This study indicated that using the Apriori algorithm and defining the statistic d was a novel way to refine the GSEA results. We hope to convey the intricacies from the computational results to the low-throughput experiments, and to plan experimental investigations specifically.

The research focuses on students’ financial knowledge, behaviors, and attitudes, which can then be analyzed using the APRIORI algorithm to identify significant patterns and associations.

The SAR difference of different confidence degree thresholds in D = 4.

Frequent two-dimensional patterns from Table 1.

The result comparison of the different D.