Association Rule Mining

Association Rule Mining

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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

Course Content

Association Rule Mining

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

Challenge: FP-growth Implementation

Task

FP-growth algorithm can be easily implemented using the mlxtend library.
You need to use fpgrowth(encoded_data, min_support) function to get frequent itemsets on the generated dataset. Use 0.05 as a minimum support value.

Note
Pay attention that we have to one-hot-encode the transaction dataset to use the FP-growth algorithm in this task.

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Section 2. Chapter 6

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Challenge: FP-growth Implementation

Task

FP-growth algorithm can be easily implemented using the mlxtend library.
You need to use fpgrowth(encoded_data, min_support) function to get frequent itemsets on the generated dataset. Use 0.05 as a minimum support value.

Note
Pay attention that we have to one-hot-encode the transaction dataset to use the FP-growth algorithm in this task.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 6

toggle bottom row

Challenge: FP-growth Implementation

Task

FP-growth algorithm can be easily implemented using the mlxtend library.
You need to use fpgrowth(encoded_data, min_support) function to get frequent itemsets on the generated dataset. Use 0.05 as a minimum support value.

Note
Pay attention that we have to one-hot-encode the transaction dataset to use the FP-growth algorithm in this task.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Task

FP-growth algorithm can be easily implemented using the mlxtend library.
You need to use fpgrowth(encoded_data, min_support) function to get frequent itemsets on the generated dataset. Use 0.05 as a minimum support value.

Note
Pay attention that we have to one-hot-encode the transaction dataset to use the FP-growth algorithm in this task.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 2. Chapter 6

Switch to desktop for real-world practiceContinue from where you are using one of the options below

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