Name
Abstract | ||
|---|---|---|
Online data mining techniques are used to uncover relevant patterns in complex data which are dynamic by nature and thus continuously extended with real-time arriving data streams. Relational association rules (RARs), a data analysis and mining concept, extend the classical association rules so as to capture different relations between the attributes characterizing the data. This paper introduces a new Incremental Relational Association Rule Mining (IRARM) approach with the aim of progressively adapting the interesting relational association rules identified in a data set, when it is enlarged with new instances. We have experimentally evaluated IRARM on publicly available data sets. The reduction in mining time when using IRARM against mining from scratch emphasizes its efficiency in adapting the rules to real-time data extension. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.procs.2018.07.216 | Procedia Computer Science |
Keywords | Field | DocType |
Data mining,Unsupervised learning,Relational association rules 2000 MSC: 6207,68T05,68P15 | Data mining,Data set,Data stream mining,Computer science,Complex data type,Association rule learning,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
126 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diana-Lucia Miholca | 1 | 7 | 3.47 |
| Gabriela Czibula | 2 | 80 | 19.53 |
| Liana Maria Crivei | 3 | 1 | 1.70 |