DFP-growth: An efficient algorithm for mining frequent patterns in dynamic database
Mining frequent patterns in a large database is still an important and relevant topic in data mining. Nowadays, FP-Growth is one of the famous and benchmarked algorithms to mine the frequent patterns from FP-Tree data structure. However, the major drawback in FP-Growth is, the FP-Tree must be rebuil...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer
2012
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/27032/ http://umpir.ump.edu.my/id/eprint/27032/ http://umpir.ump.edu.my/id/eprint/27032/1/DFP-growth-%20An%20efficient%20algorithm%20for%20mining%20frequent%20patterns%20in%20dynamic%20database.pdf |
| Summary: | Mining frequent patterns in a large database is still an important and relevant topic in data mining. Nowadays, FP-Growth is one of the famous and benchmarked algorithms to mine the frequent patterns from FP-Tree data structure. However, the major drawback in FP-Growth is, the FP-Tree must be rebuilt all over again once the original database is changed. Therefore, in this paper we introduce an efficient algorithm called Dynamic Frequent Pattern Growth (DFP-Growth) to mine the frequent patterns from dynamic database. Experiments with three UCI datasets show that the DFP-Growth is up to 1.4 times faster than benchmarked FP-Growth, thus verify it efficiencies. |
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