Scalable technique to discover items support from trie data structure
One of the popular and compact trie data structure to represent frequent patterns is via frequent pattern tree (FP-Tree). There are two scanning processes involved in the original database before the FP-Tree can be constructed. One of them is to determine the items support (items and their support)...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Springer, Berlin, Heidelberg
2012
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/27033/1/Scalable%20technique%20to%20discover%20items%20support%20from%20trie%20data%20structure.pdf http://umpir.ump.edu.my/id/eprint/27033/ https://doi.org/10.1007/978-3-642-34062-8_65 |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English |
| Summary: | One of the popular and compact trie data structure to represent frequent patterns is via frequent pattern tree (FP-Tree). There are two scanning processes involved in the original database before the FP-Tree can be constructed. One of them is to determine the items support (items and their support) that fulfill minimum support threshold by scanning the entire database. However, if the changes are suddenly occurred in the database, this process must be repeated all over again. In this paper, we introduce a technique called Fast Determination of Item Support Technique (F-DIST) to capture the items support from our proposed Disorder Support Trie Itemset (DOSTrieIT) data structure. Experiments through three UCI benchmark datasets show that the computational time to capture the items support using F-DIST from DOSTrieIT is significantly outperformed the classical FP-Tree technique about 3 orders of magnitude, thus verify its scalability. |
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