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An improvement of FP-Growth association rule mining algorithm based on adjacency table
- Source :
- MATEC Web of Conferences, Vol 189, p 10012 (2018)
- Publication Year :
- 2018
- Publisher :
- EDP Sciences, 2018.
-
Abstract
- FP-Growth algorithm is an association rule mining algorithm based on frequent pattern tree (FP-Tree), which doesn’t need to generate a large number of candidate sets. However, constructing FP-Tree requires two scansof the original transaction database and the recursive mining of FP-Tree to generate frequent itemsets. In addition, the algorithm can’t work effectively when the dataset is dense. To solve the problems of large memory usage and low time-effectiveness of data mining in this algorithm, this paper proposes an improved algorithm based on adjacency table using a hash table to store adjacency table, which considerably saves the finding time. The experimental results show that the improved algorithm has good performance especially for mining frequent itemsets in dense data sets.
- Subjects :
- Engineering (General). Civil engineering (General)
TA1-2040
Subjects
Details
- Language :
- English, French
- ISSN :
- 2261236X
- Volume :
- 189
- Database :
- Directory of Open Access Journals
- Journal :
- MATEC Web of Conferences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.0258581bb4914539b9fccc3e41a5bf63
- Document Type :
- article
- Full Text :
- https://doi.org/10.1051/matecconf/201818910012