Name
Abstract | ||
|---|---|---|
This paper introduces a highly efficient pattern mining technique called Clustering-Based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in transaction databases using clustering techniques. The set of transactions are first clus-tered using the k-means algorithm, where highly correlated transactions are grouped together. Next, the relevant patterns are derived by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one approximate and one exact. We demonstrate the efficiency and effectiveness of CBPM through a thorough experimental evaluation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDE.2019.00163 | 2019 IEEE 35th International Conference on Data Engineering (ICDE) |
Keywords | Field | DocType |
Approximation algorithms,Clustering algorithms,Data mining,Runtime,Itemsets | Approximation algorithm,Data mining,Computer science,Data mining algorithm,Cluster analysis,Database transaction | Conference |
ISSN | ISBN | Citations |
1084-4627 | 978-1-5386-7474-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Youcef Djenouri | 1 | 300 | 32.51 |
| Chun-Wei Lin | 2 | 1484 | 154.11 |
| Kjetil Nørvåg | 3 | 1311 | 79.26 |
| Heri Ramampiaro | 4 | 154 | 20.46 |