Association Rule Mining

1. Introduction to Association Rule Mining

Delve into the foundational principles of uncovering hidden connections within vast datasets. Explore key metrics like support, confidence, and lift, illuminating the significance of association rule mining across industries. Through engaging discussions and real-world examples, gain insight into this essential analytical technique, driving strategic decision-making and uncovering actionable insights.

Definition and Overview of ARM

Frequent Itemsets and Association rules

Support, Confidence, and Lift Measures

Challenge: Metrics Calculation

Apriori Principle and Its Significance

2. Mining Frequent Itemsets

We will cover essential techniques for identifying recurring item combinations in large datasets. Learners explore the Apriori and FP-growth algorithms and learn to assess the significance of discovered associations using metrics like support, confidence, and lift. Participants gain insights into efficiently extracting meaningful patterns from transactional data through practical examples.

Apriori Algorithm

Challenge: Apriori Algorithm Implementation

Optimization Techniques for Efficient Itemset Mining

FP-growth Algorithm.FP-tree

FP-growth Algorithm

Challenge: FP-growth Implementation

How to Choose Minimum Support/Confidence Values

3. Additional Applications of ARM

We will explore some domains where ARM techniques find practical utility beyond their traditional uses, for example, in recommendation systems or classification tasks. By uncovering hidden patterns and relationships within complex datasets, ARM offers valuable insights and facilitates informed decision-making across various fields, ultimately driving innovation and efficiency.

Exploratory Data Analysis

Recommendation Systems

Other Applications

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 the data argument represents the transaction dataset in one-hot-encoded format. The min_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. The frequent_itemsets variable represents a list of frequent itemsets generated by the apriori function, and the metric variable represents the metric name in a string format that we use to measure the strength of the association rule. The min_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:

Transaction Data

Transaction	Items
1	milk, bread, eggs
2	bread, butter
3	milk, bread, butter
4	milk, eggs
5	bread, eggs, butter
6	bread, eggs
7	bread, milk, eggs, butter

We want to convert the "Items" column into a one-hot encoded format.

After One-Hot Encoding:

Transaction Data

Transaction	bread	butter	eggs	milk
1	1	0	1	1
2	1	1	0	0
3	1	1	0	1
4	0	0	1	1
5	1	1	1	0
6	1	0	1	0
7	1	1	1	1

Tarefa

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.

Tudo estava claro?

Seção 2. Capítulo 2

Conteúdo do Curso

Association Rule Mining

1. Introduction to Association Rule Mining

Definition and Overview of ARM Frequent Itemsets and Association rules Support, Confidence, and Lift Measures Challenge: Metrics Calculation Apriori Principle and Its Significance

2. Mining Frequent Itemsets

Apriori Algorithm Challenge: Apriori Algorithm Implementation Optimization Techniques for Efficient Itemset Mining FP-growth Algorithm.FP-tree FP-growth Algorithm Challenge: FP-growth Implementation How to Choose Minimum Support/Confidence Values

3. Additional Applications of ARM

Exploratory Data Analysis Recommendation Systems Other Applications

Association Rule Mining

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 the data argument represents the transaction dataset in one-hot-encoded format. The min_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. The frequent_itemsets variable represents a list of frequent itemsets generated by the apriori function, and the metric variable represents the metric name in a string format that we use to measure the strength of the association rule. The min_threshold argument represents the minimum threshold value of the metric to detect significant association rules.

What is one-hot-encoded format

Suppose we have the following transaction dataset for Apriori algorithm:

Transaction Data

Transaction	Items
1	milk, bread, eggs
2	bread, butter
3	milk, bread, butter
4	milk, eggs
5	bread, eggs, butter
6	bread, eggs
7	bread, milk, eggs, butter

We want to convert the "Items" column into a one-hot encoded format.

After One-Hot Encoding:

Transaction Data

Transaction	bread	butter	eggs	milk
1	1	0	1	1
2	1	1	0	0
3	1	1	0	1
4	0	0	1	1
5	1	1	1	0
6	1	0	1	0
7	1	1	1	1

Tarefa

Tudo estava claro?

Seção 2. Capítulo 2