Contenido del Curso
Association Rule Mining
Association Rule Mining
3. Additional Applications of ARM
Challenge: Apriori Algorithm Implementation
Now we will implement Apriori algorithm using mlxtend
library.
Let's discover some implementation key points:
- We will utilize the
mlxtend.frequent_patterns
module to detect frequent itemsets using the Apriori algorithm and to provide association rules; - The Apriori algorithm is implemented using the
apriori(data, min_support, use_colnames=True)
function. Note that thedata
argument represents the transaction dataset in one-hot-encoded format. Themin_support
argument is a numerical value that represents the minimum support threshold; - To detect association rules, we can use the
association_rules(frequent_itemsets, metric, min_threshold)
function. Thefrequent_itemsets
variable represents a list of frequent itemsets generated by theapriori
function, and themetric
variable represents the metric name in a string format that we use to measure the strength of the association rule. Themin_threshold
argument represents the minimum threshold value of the metric to detect significant association rules.
What is one-hot-encoded format
One-hot encoding is a technique used to convert categorical variables into a numerical format that can be used for machine learning algorithms. It involves representing each category in a categorical variable as a binary vector, where each vector has a length equal to the number of unique categories in the variable. The vector is all zeros except for the index corresponding to the category, which is set to 1.
Suppose we have the following transaction dataset for Apriori algorithm:
We want to convert the "Items" column into a one-hot encoded format.
After One-Hot Encoding:
Tarea
Your task is to find frequent itemsets and association rules in the given dataset. You need to use the apriori()
function with one-hot-encoded data and a minimum support value of 0.2
as arguments to detect frequent itemsets. Then, use the association_rules()
function with the frequent itemsets, confidence, and minimum threshold value of 0.7
as arguments to detect association rules.
Tarea
Your task is to find frequent itemsets and association rules in the given dataset. You need to use the apriori()
function with one-hot-encoded data and a minimum support value of 0.2
as arguments to detect frequent itemsets. Then, use the association_rules()
function with the frequent itemsets, confidence, and minimum threshold value of 0.7
as arguments to detect association rules.
¿Todo estuvo claro?
Challenge: Apriori Algorithm Implementation
Now we will implement Apriori algorithm using mlxtend
library.
Let's discover some implementation key points:
- We will utilize the
mlxtend.frequent_patterns
module to detect frequent itemsets using the Apriori algorithm and to provide association rules; - The Apriori algorithm is implemented using the
apriori(data, min_support, use_colnames=True)
function. Note that thedata
argument represents the transaction dataset in one-hot-encoded format. Themin_support
argument is a numerical value that represents the minimum support threshold; - To detect association rules, we can use the
association_rules(frequent_itemsets, metric, min_threshold)
function. Thefrequent_itemsets
variable represents a list of frequent itemsets generated by theapriori
function, and themetric
variable represents the metric name in a string format that we use to measure the strength of the association rule. Themin_threshold
argument represents the minimum threshold value of the metric to detect significant association rules.
What is one-hot-encoded format
One-hot encoding is a technique used to convert categorical variables into a numerical format that can be used for machine learning algorithms. It involves representing each category in a categorical variable as a binary vector, where each vector has a length equal to the number of unique categories in the variable. The vector is all zeros except for the index corresponding to the category, which is set to 1.
Suppose we have the following transaction dataset for Apriori algorithm:
We want to convert the "Items" column into a one-hot encoded format.
After One-Hot Encoding:
Tarea
Your task is to find frequent itemsets and association rules in the given dataset. You need to use the apriori()
function with one-hot-encoded data and a minimum support value of 0.2
as arguments to detect frequent itemsets. Then, use the association_rules()
function with the frequent itemsets, confidence, and minimum threshold value of 0.7
as arguments to detect association rules.
Tarea
Your task is to find frequent itemsets and association rules in the given dataset. You need to use the apriori()
function with one-hot-encoded data and a minimum support value of 0.2
as arguments to detect frequent itemsets. Then, use the association_rules()
function with the frequent itemsets, confidence, and minimum threshold value of 0.7
as arguments to detect association rules.
¿Todo estuvo claro?
Now we will implement Apriori algorithm using mlxtend
library.
Let's discover some implementation key points:
- We will utilize the
mlxtend.frequent_patterns
module to detect frequent itemsets using the Apriori algorithm and to provide association rules; - The Apriori algorithm is implemented using the
apriori(data, min_support, use_colnames=True)
function. Note that thedata
argument represents the transaction dataset in one-hot-encoded format. Themin_support
argument is a numerical value that represents the minimum support threshold; - To detect association rules, we can use the
association_rules(frequent_itemsets, metric, min_threshold)
function. Thefrequent_itemsets
variable represents a list of frequent itemsets generated by theapriori
function, and themetric
variable represents the metric name in a string format that we use to measure the strength of the association rule. Themin_threshold
argument represents the minimum threshold value of the metric to detect significant association rules.
What is one-hot-encoded format
One-hot encoding is a technique used to convert categorical variables into a numerical format that can be used for machine learning algorithms. It involves representing each category in a categorical variable as a binary vector, where each vector has a length equal to the number of unique categories in the variable. The vector is all zeros except for the index corresponding to the category, which is set to 1.
Suppose we have the following transaction dataset for Apriori algorithm:
We want to convert the "Items" column into a one-hot encoded format.
After One-Hot Encoding:
Tarea
Your task is to find frequent itemsets and association rules in the given dataset. You need to use the apriori()
function with one-hot-encoded data and a minimum support value of 0.2
as arguments to detect frequent itemsets. Then, use the association_rules()
function with the frequent itemsets, confidence, and minimum threshold value of 0.7
as arguments to detect association rules.
Challenge: Apriori Algorithm Implementation
Now we will implement Apriori algorithm using mlxtend
library.
Let's discover some implementation key points:
- We will utilize the
mlxtend.frequent_patterns
module to detect frequent itemsets using the Apriori algorithm and to provide association rules; - The Apriori algorithm is implemented using the
apriori(data, min_support, use_colnames=True)
function. Note that thedata
argument represents the transaction dataset in one-hot-encoded format. Themin_support
argument is a numerical value that represents the minimum support threshold; - To detect association rules, we can use the
association_rules(frequent_itemsets, metric, min_threshold)
function. Thefrequent_itemsets
variable represents a list of frequent itemsets generated by theapriori
function, and themetric
variable represents the metric name in a string format that we use to measure the strength of the association rule. Themin_threshold
argument represents the minimum threshold value of the metric to detect significant association rules.
What is one-hot-encoded format
One-hot encoding is a technique used to convert categorical variables into a numerical format that can be used for machine learning algorithms. It involves representing each category in a categorical variable as a binary vector, where each vector has a length equal to the number of unique categories in the variable. The vector is all zeros except for the index corresponding to the category, which is set to 1.
Suppose we have the following transaction dataset for Apriori algorithm:
We want to convert the "Items" column into a one-hot encoded format.
After One-Hot Encoding:
Tarea
Your task is to find frequent itemsets and association rules in the given dataset. You need to use the apriori()
function with one-hot-encoded data and a minimum support value of 0.2
as arguments to detect frequent itemsets. Then, use the association_rules()
function with the frequent itemsets, confidence, and minimum threshold value of 0.7
as arguments to detect association rules.