How FP tree is better than Apriori Algorithm?
1 Answer

Apriori Algorithm :

  • It is a classic algorithm for learning association rules.
  • It uses a bottom up approach where frequent subsets are extended one at a time.
  • It uses Breadth first search and hash tree structure to count candidate item sets efficiently.

FP Growth:

  • It allows frequent item set discovery without candidate generation.
  • It builds a compact data structure called FP tree with two passes over thedatabase.
  • It extracts frequent item sets directly from the FP tree and traverses through the FP tree.

This comparative study shows how FP(Frequent Pattern) Tree is better than Apriori Algorithm.

Parameters Apriori Algorithm Fp tree
Technique Use Apriori,join and prune property. It constructs conditional,frequent pattern tree and conditional pattern base from database which satisfy minimum support
Memory utilization It requires large amount of memory space due to large number of candidates generated. It requires small amount of memory space due to compact structure and no candidate generation.
No of scans Multiple scans for generating candidate sets. Scans the Database only twice.
Time Execution time is more as time is wasted in producing candidates every time. Execution time is lesser than Apriori due to the absence of candidates.
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