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Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm
- Source :
- IEEE Access, Vol 9, Pp 55726-55741 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- 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.
- Subjects :
- Association rule mining
FP-tree
General Computer Science
Association rule learning
Computer science
business.industry
General Engineering
InformationSystems_DATABASEMANAGEMENT
frequent itemset mining
data mining
Execution time
Maintenance engineering
Tree (data structure)
Resource (project management)
Business intelligence
FPISC-tree
General Materials Science
lcsh:Electrical engineering. Electronics. Nuclear engineering
Cluster analysis
business
Algorithm
lcsh:TK1-9971
FP-growth
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....5a8bef5af5deddbb64f8d6d96d455f20