Fast Determination of Items Support Technique from Enhanced Tree Data Structure
Frequent Pattern Tree (FP-Tree) is one of the famous data structure to keep frequent itemsets. However when the content of transactional database is modified, FP-Tree must be reconstructed again due to the changes in patterns and items support. Until this recent, most of the techniques in frequent p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SERSC
2014
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/6612/ http://umpir.ump.edu.my/id/eprint/6612/ http://umpir.ump.edu.my/id/eprint/6612/ http://umpir.ump.edu.my/id/eprint/6612/1/2.pdf |
| Summary: | Frequent Pattern Tree (FP-Tree) is one of the famous data structure to keep frequent itemsets. However when the content of transactional database is modified, FP-Tree must be reconstructed again due to the changes in patterns and items support. Until this recent, most of the techniques in frequent pattern mining are using the original database to determine the items support and not from their recommended trees data structure. Therefore in this paper, we proposed a technique called Fast Determination of Item Support Technique (F-DIST) to capture the items support from our suggested Disorder Support Trie Itemset (DOSTrieIT) data structure. Experiments with the UCI datasets show that the processing time to determine the items support using F-DIST from DOSTrieIT is outperformed the classical FP-Tree technique. Furthermore, the processing time to construct a complete tree data structure for DOSTrieIT is lesser than the benchmarked CanTree data structure. |
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