Dataset

This dataset was created by Honey Patel

Contents

Association rule is an important technique in data mining.

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 ...

Additional file 2. The integral table of transactions T.

Additional file 3. The integral matrix of concordance indices.

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 ---

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).

The hyperparameters of the apriori algorithm.

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

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

Context The Groceries Market Basket Dataset, which can be found here. The dataset contains 9835 transactions by customers shopping for groceries. The data contains 169 unique items.
The data is suitable to do data mining for market basket analysis which has multiple variables.

Acknowledgement Thanks to https://github.com/shubhamjha97/association-rule-mining-apriori
The data is under course Association rules mining using Apriori algorithm. Course Assignment for CS F415- Data Mining @ BITS Pilani, Hyderabad Campus. Done under the guidance of Dr. Aruna Malapati, Assistant Professor, BITS Pilani, Hyderabad Campus.

Pre-processing

The csv file was read transaction by transaction and each transaction was saved as a list. A mapping was created from the unique items in the dataset to integers so that each item corresponded to a unique integer. The entire data was mapped to integers to reduce the storage and computational requirement. A reverse mapping was created from the integers to the item, so that the item names could be written in the final output file.

Don't forget to upvote before you download :)

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

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.

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

MATLAB Codes and original data for Apriori

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

Background: Treatment effect of current pharmacotherapies for migraine is unsatisfying. Discovering new anti-migraine natural products and nutraceuticals from large collections of Chinese medicine classical literature may assist to address this gap.Methods: We conducted a comprehensive search in the Encyclopedia of Traditional Chinese Medicine (version 5.0) to obtain migraine-related citations, then screened and scored these citations to identify clinical management of migraine using oral herbal medicine in history. Information of formulae, herbs and symptoms were further extracted. After standardisation, these data were analysed using frequency analysis and the Apriori algorithm. Anti-migraine effects and mechanisms of actions of the main herbs and formula were summarised.Results: Among 614 eligible citations, the most frequently used formula was chuan xiong cha tiao san (CXCTS), and the most frequently used herb was chuan xiong. Dietary medicinal herbs including gan cao, bai zhi, bo he, tian ma and sheng jiang were identified. Strong associations were constructed among the herb ingredients of CXCTS formula. Symptoms of chronic duration and unilateral headache were closely related with herbs of chuan xiong, gan cao, fang feng, qiang huo and cha. Symptoms of vomiting and nausea were specifically related to herbs of sheng jiang and ban xia.Conclusion: The herb ingredients of CXCTS which presented anti-migraine effects with reliable evidence of anti-migraine actions can be selected as potential drug discovery candidates, while dietary medicinal herbs including sheng jiang, bo he, cha, bai zhi, tian ma, and gan cao can be further explored as nutraceuticals for migraine.

Variable description.

The one-item SAR of D = 3.

The result comparison of the different D.

The SAR difference of different confidence degree thresholds in D = 3.

Background: As time evolved, traditional Chinese medicine (TCM) became integrated into the global medical system as complementary treatments. Some essential TCM herbs started to play a limited role in clinical practices because of Western medication development. For example, Fuzi (Aconiti Lateralis Radix Praeparata) is a toxic but indispensable TCM herb. Fuzi was mainly used in poor circulation and life-threatening conditions by history records. However, with various Western medication options for treating critical conditions currently, how is Fuzi used clinically and its indications in modern TCM are unclear. This study aimed to evaluate Fuzi and Fuzi-based formulas in modern clinical practices using artificial intelligence and data mining methods.Methods: This nationwide descriptive study with market basket analysis used a cohort selected from the Taiwan National Health Insurance database that contained one million national representatives between 2003 and 2010 used for our analysis. Descriptive statistics were performed to demonstrate the modern clinical indications of Fuzi. Market basket analysis was calculated by the Apriori algorithm to discover the association rules between Fuzi and other TCM herbs.Results: A total of 104,281 patients using 405,837 prescriptions of Fuzi and Fuzi-based formulas were identified. TCM doctors were found to use Fuzi in pulmonary (21.5%), gastrointestinal (17.3%), and rheumatologic (11.0%) diseases, but not commonly in cardiovascular diseases (7.4%). Long-term users of Fuzi and Fuzi-based formulas often had the following comorbidities diagnosed by Western doctors: osteoarthritis (31.0%), peptic ulcers (29.5%), hypertension (19.9%), and COPD (19.7%). Patients also used concurrent medications such as H2-receptor antagonists, nonsteroidal anti-inflammatory drugs, β-blockers, calcium channel blockers, and aspirin. Through market basket analysis, for the first time, we noticed many practical Fuzi-related herbal pairs such as Fuzi–Hsihsin (Asari Radix et Rhizoma)–Dahuang (Rhei Radix et Rhizoma) for neurologic diseases and headache.Conclusion: For the first time, big data analysis was applied to uncover the modern clinical indications of Fuzi in addition to traditional use. We provided necessary evidence on the scientific use of Fuzi in current TCM practices, and the Fuzi-related herbal pairs discovered in this study are helpful to the development of new botanical drugs.

The discretization results of D = 3.