Contenido del Curso
Association Rule Mining
3. Additional Applications of ARM
Association Rule Mining
Frequent Itemsets and Association rules
Frequent itemsets refer to sets of items that frequently appear together in transactions within a dataset. In other words, these are combinations of items that occur with a frequency above a specified threshold.
In a transactional dataset from a grocery store, if the combination of items {milk, bread, eggs} occurs frequently enough, exceeding the minimum support threshold, it qualifies as a frequent itemset. This suggests that customers commonly buy milk, bread, and eggs together.
By analyzing such patterns, we can predict store revenue and adjust production lines to ensure an adequate supply of these products.
Association rules are logical relationships that describe the associations between different sets of items in a dataset. These rules are typically expressed in the form of if-then statements, indicating that the presence of certain items (the antecedent) implies the presence of other items (the consequent) with a certain level of confidence.
Continuing with the grocery store dataset, let's consider the association rule {bread, eggs} -> {milk}. This rule indicates that when a customer buys both bread and eggs (the antecedent), there's a high chance they'll also purchase milk (the consequent). For example, if bread and eggs frequently go together in transactions, customers may be prompted to also pick up milk.
This suggests that grouping these items together in the store could boost milk sales, presenting an opportunity to enhance revenue.
In essence, frequent itemsets highlight item combinations that occur frequently, while association rules uncover patterns and relationships between items within these frequent itemsets.
¿Todo estuvo claro?
Contenido del Curso
Association Rule Mining
3. Additional Applications of ARM
Association Rule Mining
Frequent Itemsets and Association rules
Frequent itemsets refer to sets of items that frequently appear together in transactions within a dataset. In other words, these are combinations of items that occur with a frequency above a specified threshold.
In a transactional dataset from a grocery store, if the combination of items {milk, bread, eggs} occurs frequently enough, exceeding the minimum support threshold, it qualifies as a frequent itemset. This suggests that customers commonly buy milk, bread, and eggs together.
By analyzing such patterns, we can predict store revenue and adjust production lines to ensure an adequate supply of these products.
Association rules are logical relationships that describe the associations between different sets of items in a dataset. These rules are typically expressed in the form of if-then statements, indicating that the presence of certain items (the antecedent) implies the presence of other items (the consequent) with a certain level of confidence.
Continuing with the grocery store dataset, let's consider the association rule {bread, eggs} -> {milk}. This rule indicates that when a customer buys both bread and eggs (the antecedent), there's a high chance they'll also purchase milk (the consequent). For example, if bread and eggs frequently go together in transactions, customers may be prompted to also pick up milk.
This suggests that grouping these items together in the store could boost milk sales, presenting an opportunity to enhance revenue.
In essence, frequent itemsets highlight item combinations that occur frequently, while association rules uncover patterns and relationships between items within these frequent itemsets.
¿Todo estuvo claro?
