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Association rule analysis - FP Growth Algorithm

The Apriori algorithm generates candidates and tests if they are frequent. The generation of candidate itemsets and support counting are expensive in both space and time.

The FP-Growth algorithm allows frequent itemset discovery without candidate itemsets generation. 
The FP growth tree is constructed by 
  • Scanning data and find support for each item.
  • Discarding the infrequent items.
  • Sorting the frequent items in decreasing order based on their support.
  • Building a compact data structure called the FP-tree to map all transactions to a path in FP tree.
The height of the tree is bounded by the maximum number of items in a transaction. 
FP growth is an efficient mining method of frequent patterns in large Database : using a highly compact FP‐tree, divide‐and‐conquer method in nature.
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