Paper Info
Title
DynGRAR - A dynamic approach to mining gradual relational association rules.
Abstract
Relational Association Rules (RARs) capture generic relations between attributes values in possibly large data sets. Due to their ability to uncover underlying semantically relevant patterns, they are of particular interest in data mining research and applicable in both unsupervised and supervised learning scenarios. With the aim of increasing the stability and expressiveness of the classical, non-gradual RARs, Gradual Relational Association Rules (GRARs) have been introduced. By generalizing the boolean relations to gradual relations, GRARs also capture the degrees to which generic relations are satisfied. In the current paper we introduce a new approach called DynGRAR (Dynamic Gradual Relational Association Rules Miner) for uncovering interesting GRARs in dynamic data sets which are incrementally extended with both new data instances and new data attributes. DynGRAR dynamically adjusts the set of all interesting GRARs. Through multiple experiments performed on publicly available software defect prediction data sets, we have evaluated DynGRAR versus applying the standard GRARs mining algorithm from scratch on the extended data. The results obtained emphasize the superior performance of the dynamic approach we propose.
Year
DOI
Venue
2019
10.1016/j.procs.2019.09.155
Procedia Computer Science
Keywords
Field
DocType
Unuspervised learning,data mining,gradual relational association rule 2000 MSC: 68T05,03B52,68T35
Data mining,Data set,Computer science,Generalization,Software bug,Supervised learning,Dynamic data,Association rule learning,Artificial intelligence,Data mining algorithm,Machine learning,Expressivity
Conference
Volume
Issue
ISSN
159
C
1877-0509
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Diana-Lucia Miholca173.47
Gabriela Czibula28019.53