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FP-Growth avoids scanning the entire database repeatedly.
The good side of Apriori algorithm is that as you can see from the previous chapter,, we prune the search space by eliminating candidate patterns. We don’t generate any patterns that contain non-frequent patterns. However, we keep scanning the entire dataset again and again, which is very memory and computation intensive.
So, can we just scan once or twice and generate frequent patterns?
Yes, there is a such algorithm called Frequent pattern growth. It finds the largest possible frequent pattern and construct smaller frequent patterns out of it. That’s why it is also called a death-first search algorithm By doing so, it also avoid generating candidates explicitly eliminating repeated data scanning.
We will now demonstrate how frequent itemsets are mined using FP-algorithm. The first step is to generate a Frequent pattern tree which is a compact representation of the entire dataset containing all information needed for generating frequent patterns.
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The next step is mining frequent patterns from the frequent pattern tree.
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