Conteúdo do Curso
Association Rule Mining
3. Additional Applications of ARM
Association Rule Mining
Definition and Overview of ARM
It is strongly recommended to finish Introduction to Python course before starting this one.
Go here if needed.
Association Rule Mining (ARM) is a powerful data mining technique to discover interesting relationships, associations, or patterns within large transactional datasets. At its core, ARM seeks to unveil frequent itemsets and generate meaningful association rules that capture dependencies between different items.
A transactional dataset is a structured collection of records, where each record represents a single transaction or event. Each transaction consists of a set of items, attributes, or variables, providing information about the entities involved and the interactions or actions that occurred during the transaction.
ARM involves the systematic exploration of transactional data to identify frequent itemsets, which are sets of items that co-occur together frequently in transactions. These frequent itemsets serve as the basis for generating association rules, which capture meaningful relationships between items.
ARM applications
- Retail and E-commerce: In retail, ARM is used for market basket analysis, optimizing product placement, personalized recommendations, and targeted marketing strategies. E-commerce platforms utilize ARM to understand customer behavior, improve cross-selling opportunities, and enhance the online shopping experience;
- Healthcare: ARM helps in analyzing patient treatment data to optimize treatment plans, identify potential risk factors, and improve patient care outcomes. It assists healthcare providers in identifying associations between medical procedures, diagnoses, and patient outcomes, leading to better decision-making and personalized treatment strategies;
- Finance and Banking: In finance, ARM is crucial for fraud detection, risk management, and personalized financial services. Banks leverage ARM to detect unusual patterns in transaction data, identify potential fraudulent activities, and personalize financial product offerings based on customer behavior and preferences.
In 1992, a retail consulting group led by Thomas Blischok conducted a study of 1.2 million transactions across 25 stores for the retailer Osco Drug, under the company Teradata.
After analyzing all these transactions, the strongest rule that emerged was "Beer and diapers are most often purchased together between 5:00 and 7:00 PM."
Despite the counterintuitive nature of this rule, an explanation for the beer-diapers pair was found: when both members of a young family returned home from work (around 5:00 PM), wives usually sent husbands to the nearest store for diapers. And husbands, without much thought, combined the useful task of buying diapers with the pleasant task of purchasing beer for their own evening entertainment.
Tudo estava claro?
Conteúdo do Curso
Association Rule Mining
3. Additional Applications of ARM
Association Rule Mining
Definition and Overview of ARM
It is strongly recommended to finish Introduction to Python course before starting this one.
Go here if needed.
Association Rule Mining (ARM) is a powerful data mining technique to discover interesting relationships, associations, or patterns within large transactional datasets. At its core, ARM seeks to unveil frequent itemsets and generate meaningful association rules that capture dependencies between different items.
A transactional dataset is a structured collection of records, where each record represents a single transaction or event. Each transaction consists of a set of items, attributes, or variables, providing information about the entities involved and the interactions or actions that occurred during the transaction.
ARM involves the systematic exploration of transactional data to identify frequent itemsets, which are sets of items that co-occur together frequently in transactions. These frequent itemsets serve as the basis for generating association rules, which capture meaningful relationships between items.
ARM applications
- Retail and E-commerce: In retail, ARM is used for market basket analysis, optimizing product placement, personalized recommendations, and targeted marketing strategies. E-commerce platforms utilize ARM to understand customer behavior, improve cross-selling opportunities, and enhance the online shopping experience;
- Healthcare: ARM helps in analyzing patient treatment data to optimize treatment plans, identify potential risk factors, and improve patient care outcomes. It assists healthcare providers in identifying associations between medical procedures, diagnoses, and patient outcomes, leading to better decision-making and personalized treatment strategies;
- Finance and Banking: In finance, ARM is crucial for fraud detection, risk management, and personalized financial services. Banks leverage ARM to detect unusual patterns in transaction data, identify potential fraudulent activities, and personalize financial product offerings based on customer behavior and preferences.
In 1992, a retail consulting group led by Thomas Blischok conducted a study of 1.2 million transactions across 25 stores for the retailer Osco Drug, under the company Teradata.
After analyzing all these transactions, the strongest rule that emerged was "Beer and diapers are most often purchased together between 5:00 and 7:00 PM."
Despite the counterintuitive nature of this rule, an explanation for the beer-diapers pair was found: when both members of a young family returned home from work (around 5:00 PM), wives usually sent husbands to the nearest store for diapers. And husbands, without much thought, combined the useful task of buying diapers with the pleasant task of purchasing beer for their own evening entertainment.
Tudo estava claro?
