Paper Info
Title
Mining triadic association rules from ternary relations
Abstract
Ternary and more generally n-ary relations are commonly found in real-life applications and data collections. In this paper, we define new notions and propose procedures to mine closed tri-sets (triadic concepts) and triadic association rules within the framework of triadic concept analysis. The input data is represented as a formal triadic context of the form K := (K1,K2,K3, Y), where K1, K2, and K3 are object, attribute and condition sets respectively, and Y is a ternary relation between the three sets. While dyadic association rules represent links between two groups of attributes (itemsets), triadic association rules can take at least three distinct forms. One of them is the following: A → C D, where A and D are subsets of K2, and C is a subset of K3. It states that A implies D under the conditions in C. In particular, the implication holds for any subset in C. The benefits of triadic association rules of this kind lie in the fact that they represent patterns in a more compact and meaningful way than association rules that can be extracted for example from the formal (dyadic) context K(1) := (K1, K2 × K3, Y(1)) with (ai, (aj, ak)) ε Y(1) : ⇔ (ai, aj, ak) ε Y.
Year
DOI
Venue
2011
10.1007/978-3-642-20514-9_16
ICFCA
Keywords
Field
DocType
data collection,form k,formal triadic context,triadic concept,triadic association rule,triadic concept analysis,context k,ternary relation,dyadic association rule,association rule,mining triadic association rule,c d,concept analysis
Discrete mathematics,Combinatorics,Of the form,Ternary relation,Hasse diagram,Ternary operation,Association rule learning,Formal concept analysis,Mathematics
Conference
Volume
ISSN
Citations 
6628
0302-9743
8
PageRank 
References 
Authors
0.48
13
2
Name
Order
Citations
PageRank
Rokia Missaoui1983136.45
Léonard Kwuida25516.25