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

Зміст курсу

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: Metrics Calculation

Завдання

Swipe to show code editor

In this challenge, you will implement functions to calculate all metrics discovered in the previous chapter.
We will store transactions as a list of itemsets. You have to fill in the gaps in code to calculate support, confidence and lift measures.

_{Once you've completed this task, click the button below the code to check your solution.}

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 1. Розділ 4

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Challenge: Metrics Calculation

Завдання

Swipe to show code editor

In this challenge, you will implement functions to calculate all metrics discovered in the previous chapter.
We will store transactions as a list of itemsets. You have to fill in the gaps in code to calculate support, confidence and lift measures.

_{Once you've completed this task, click the button below the code to check your solution.}

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 1. Розділ 4

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

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