Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm
One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa. As a result, some previous association rules may become invalid and some new associati...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9399130/ |
| _version_ | 1818416786019713024 |
|---|---|
| author | Wannasiri Thurachon Worapoj Kreesuradej |
| author_facet | Wannasiri Thurachon Worapoj Kreesuradej |
| author_sort | Wannasiri Thurachon |
| collection | DOAJ |
| description | One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa. As a result, some previous association rules may become invalid and some new association rules may emerge. We designed a new, more efficient approach for incremental association rule mining using a Fast Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth), a new Incremental Conditional Pattern tree (ICP-tree), and a compact sub-tree suitable for incremental mining of frequent itemsets. This algorithm retrieves previous frequent itemsets that have already been mined from the original database and their support counts then use them to efficiently mine frequent itemsets from the updated database and ICP-tree, reducing the number of rescans of the original database. Our algorithm reduced usages of resource and time for unnecessary sub-tree construction compared to individual FP- Growth, FUFP-tree maintenance, Pre-FUFP, and FCFPIM algorithms. From the results, at 3% minimum support threshold, the average execution time for pattern growth mining of our algorithm performs 46% faster than FP- Growth, FUFP-tree, Pre-FUFP, and FCFPIM. This approach to incremental association rule mining and our experimental findings may directly benefit designers and developers of computer business intelligence methods. |
| first_indexed | 2024-12-14T11:56:25Z |
| format | Article |
| id | doaj.art-a9e7f93ad8b5404c9c5aaa97a60db5aa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:56:25Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a9e7f93ad8b5404c9c5aaa97a60db5aa2022-12-21T23:02:06ZengIEEEIEEE Access2169-35362021-01-019557265574110.1109/ACCESS.2021.30717779399130Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth AlgorithmWannasiri Thurachon0https://orcid.org/0000-0002-7729-0283Worapoj Kreesuradej1https://orcid.org/0000-0002-6275-1921Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, ThailandFaculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, ThailandOne of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa. As a result, some previous association rules may become invalid and some new association rules may emerge. We designed a new, more efficient approach for incremental association rule mining using a Fast Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth), a new Incremental Conditional Pattern tree (ICP-tree), and a compact sub-tree suitable for incremental mining of frequent itemsets. This algorithm retrieves previous frequent itemsets that have already been mined from the original database and their support counts then use them to efficiently mine frequent itemsets from the updated database and ICP-tree, reducing the number of rescans of the original database. Our algorithm reduced usages of resource and time for unnecessary sub-tree construction compared to individual FP- Growth, FUFP-tree maintenance, Pre-FUFP, and FCFPIM algorithms. From the results, at 3% minimum support threshold, the average execution time for pattern growth mining of our algorithm performs 46% faster than FP- Growth, FUFP-tree, Pre-FUFP, and FCFPIM. This approach to incremental association rule mining and our experimental findings may directly benefit designers and developers of computer business intelligence methods.https://ieeexplore.ieee.org/document/9399130/Association rule miningdata miningFP-treeFP-growthFPISC-treefrequent itemset mining |
| spellingShingle | Wannasiri Thurachon Worapoj Kreesuradej Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm IEEE Access Association rule mining data mining FP-tree FP-growth FPISC-tree frequent itemset mining |
| title | Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm |
| title_full | Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm |
| title_fullStr | Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm |
| title_full_unstemmed | Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm |
| title_short | Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm |
| title_sort | incremental association rule mining with a fast incremental updating frequent pattern growth algorithm |
| topic | Association rule mining data mining FP-tree FP-growth FPISC-tree frequent itemset mining |
| url | https://ieeexplore.ieee.org/document/9399130/ |
| work_keys_str_mv | AT wannasirithurachon incrementalassociationruleminingwithafastincrementalupdatingfrequentpatterngrowthalgorithm AT worapojkreesuradej incrementalassociationruleminingwithafastincrementalupdatingfrequentpatterngrowthalgorithm |