FP-growth Algorithm
The FP-growth algorithm is a data mining technique used for frequent itemset mining. It constructs a compact an FP-tree to efficiently mine frequent itemsets by traversing the tree and identifying conditional patterns with support values above a predefined threshold. This approach eliminates the need for candidate generation and multiple database scans, making it particularly efficient for large datasets.
A conditional pattern is a sequence of items that occur together in transactions or sequences, given a specific prefix. It represents the portion of a transaction database that matches a given prefix.
Let's use the FP-tree created in the previous chapter as an example:
Let's take the prefix {bread, eggs} as an example.
To find the conditional patterns for this prefix we need to identify transactions containing this prefix and then collect the items that occur after {bread, eggs} in those transactions. It can be easily done using FP-tree:
These patterns represent the items that frequently occur after {bread, eggs} in the provided transactions, along with their counts.
FP-growth vs Apriori
The main idea of the FP-growth algorithm is to traverse the FP-tree and build all possible conditional patterns that have a support value greater than a predefined threshold. This process allows for the efficient discovery of frequent itemsets without the need for costly candidate generation and testing steps, as required in traditional algorithms like Apriori.
Note
Pay attention that itemset support equals the count of the itemset divided by the total number of transactions.
| Algorithm | Advantages | Disadvantages |
|---|---|---|
| Apriori | Simple and intuitive. | Requires multiple database scans, especially for large datasets. Generates a large number of candidate itemsets, which can be computationally expensive to generate and store. |
| FP-growth | Efficient, especially for large datasets, as it requires only two scans of the database. Does not generate candidate itemsets, thus reducing the computational overhead. Utilizes a compact data structure (FP-tree) to represent frequent itemsets. | Constructing the FP-tree can be memory-intensive for certain datasets. Implementation complexity may be higher compared to Apriori. |
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Conteúdo do Curso
Association Rule Mining
3. Additional Applications of ARM
Association Rule Mining
FP-growth Algorithm
The FP-growth algorithm is a data mining technique used for frequent itemset mining. It constructs a compact an FP-tree to efficiently mine frequent itemsets by traversing the tree and identifying conditional patterns with support values above a predefined threshold. This approach eliminates the need for candidate generation and multiple database scans, making it particularly efficient for large datasets.
A conditional pattern is a sequence of items that occur together in transactions or sequences, given a specific prefix. It represents the portion of a transaction database that matches a given prefix.
Let's use the FP-tree created in the previous chapter as an example:
Let's take the prefix {bread, eggs} as an example.
To find the conditional patterns for this prefix we need to identify transactions containing this prefix and then collect the items that occur after {bread, eggs} in those transactions. It can be easily done using FP-tree:
These patterns represent the items that frequently occur after {bread, eggs} in the provided transactions, along with their counts.
FP-growth vs Apriori
The main idea of the FP-growth algorithm is to traverse the FP-tree and build all possible conditional patterns that have a support value greater than a predefined threshold. This process allows for the efficient discovery of frequent itemsets without the need for costly candidate generation and testing steps, as required in traditional algorithms like Apriori.
Note
Pay attention that itemset support equals the count of the itemset divided by the total number of transactions.
| Algorithm | Advantages | Disadvantages |
|---|---|---|
| Apriori | Simple and intuitive. | Requires multiple database scans, especially for large datasets. Generates a large number of candidate itemsets, which can be computationally expensive to generate and store. |
| FP-growth | Efficient, especially for large datasets, as it requires only two scans of the database. Does not generate candidate itemsets, thus reducing the computational overhead. Utilizes a compact data structure (FP-tree) to represent frequent itemsets. | Constructing the FP-tree can be memory-intensive for certain datasets. Implementation complexity may be higher compared to Apriori. |
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