How FP tree is better than Apriori Algorithm?
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.
- 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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