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Challenge: FP-growth Implementation | Mining Frequent Itemsets
Association Rule Mining
course content

Course Content

Association Rule Mining

Association Rule Mining

1. Introduction to Association Rule Mining
2. Mining Frequent Itemsets
3. Additional Applications of ARM

bookChallenge: 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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bookChallenge: 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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 6
toggle bottom row

bookChallenge: 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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

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 desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 2. Chapter 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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