This webpage allows to discover frequent itemsets in a transaction dataset using a brute-force approach. It can generate frequent itemsets with their support, bond and all-confidence values. The webpage is intended to be used for demonstrating these concepts to students. Thus, the code is not efficient, and this is why it is called "naive". For efficient implementations of itemset mining algorithms, you may use the SPMF software, which offers over 250 algorithms for pattern mining.
To use this page, enter your transactions below. Each line is a transaction that is a list of strings separated by spaces. A transaction is not allowed to contain the same item twice. For example, this is a transaction database:
apple orange milk
orange tomato bread
apple orange tomato bread
orange bread
apple orange tomato bread
Enter your transactions:
Then, enter the minimum support value below. It should be an integer between 1 and the number of transactions.
Then, enter the minimum bond value below. It should be a positive number greater than or equal to zero.
Then, enter the minimum all-confidence value below. It should be a positive number greater than or equal to zero.
Then, click the button below to generate the itemsets.
The results will be displayed in the table below.
| Itemset | Support | Bond | All-confidence |
|---|
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Want to learn more about pattern mining? Check the Pattern Mining course and open-source SPMF software
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