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How FP tree is better than Apriori Algorithm?
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written 8.8 years ago by
teamques10
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• modified 8.8 years ago |
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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