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- Title
Enhancing the Performance of Association Rule Generation over Dynamic Data using Incremental Tree Structures.
- Authors
Naresh, P.; Suguna, R.
- Abstract
To discover a novel and dynamic approach for frequent itemsets generation and also for generating association rules is an imperative aspect in data mining. With the fast increase in databases, new transactions added, the incremental mining is acquainted to resolve the issues of maintaining association rules in updated databases. Earlier algorithms focused on this problem which consumed more time and costly to mine and may lack at generating qualitative rules. This paper intends to analyze the tree construction like Frequent Pattern-tree (FP), Preorder Coded (POC) tree and PrePostCoded (PPC) tree for sinking overheads and time constraints. To overcome the issue of updating association rules when new transactions addition this paper proposes a dynamic frequent itemsets mining approach using Incremental Preorder Coded (IPOC) tree. The results were compared with existing ones by time, space and certainty factor (CF) of rues which were generated. This will reduce computational and scanning overheads of original dataset against addition of new transaction items and also works in an optimized way. An analysis was done on existing algorithms and compares time complexities for various standard datasets. The proposed approach shown better performance against existed ones over time and efficiency.
- Subjects
TREES; DATA mining
- Publication
International Journal of Next-Generation Computing, 2022, Vol 13, Issue 3, p550
- ISSN
2229-4678
- Publication type
Article