A frequent pattern mining algorithm based on FP-growth without generating tree
An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows. First, it compresses the database representing frequent items into a frequent-pattern tree, or FPtree,...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Universiti Utara Malaysia
2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/59759/1/PG671_676.pdf http://psasir.upm.edu.my/id/eprint/59759/ |
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| Summary: | An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows. First, it compresses the database representing frequent items into a frequent-pattern tree, or FPtree, which retains the itemset association information. It then divides the compressed database into a set of conditional databases (a special kind of projected database), each associated with one frequent item or pattern fragment, and mines each such database separately. For a large database, constructing a large tree in the memory is a time consuming task and increase the time of execution. In this paper we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well. Our algorithm works based on prime factorization, and is called Frequent Pattern-Prime Factorization (FPPF). |
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