Mining Triadic Association Rules from Ternary Relations

Missaoui, Rokia; Kwuida, Léonard

doi:10.1007/978-3-642-20514-9_16

Cited by 27 publications

(26 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To our knowledge, except for the work [17] no research has been conducted in the problem of mining triadic association rules from ternary relations. Our work helps address this issue by proposing a new, more effective approach.…”

Section: State Of the Artmentioning

confidence: 99%

“…We illustrate these definitions through an example ( Figure 1 (a)) triadic context initially proposed by [7] and taken up and adapted by [17]. This example shows a data cube with three dimensions CUSTOMERS, SUPPLIERS and PRODUCTS.…”

Section: Formal Concept Analysis and Association Rulesmentioning

confidence: 99%

“…Several approaches to research concepts and triadic analysis are shown in literature. [17] consists of input data represented as a formal triadic context transformed into a dyadic context ( figure 3). Next, a Galois lattice is constructed and requires the generation of dyadic concepts and dyadic generators and to order the succession of the latter.…”

Section: Triadic Association Rules Extractionmentioning

confidence: 99%

“…The dyadic association rules are conditional implications between two sets of proprieties called items allows to extracts hidden information on large datasets, however, the large number of rules obtained during the extraction process on several data sets leads us to seek a better match rule type. Hence the idea to propose an approach that produces the triadic association rules are more compact than the dyadic [17].…”

Section: Introductionmentioning

confidence: 99%

“…The main objective of this work was to improve and simplify the approach proposed by [17] proposing procedures to extract triadic association rules from a formal triadic context which is anensemblist representation of ternary relationship. The second goal was to make this work on the basis of formal concept analysis tools and approaches.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Mining Triadic Association Rules

Selmane

Missaoui²,

Boussaïd³

et al. 2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

Self Cite

View full text Add to dashboard Cite

show abstract

Section: State Of the Artmentioning

confidence: 99%

Section: Formal Concept Analysis and Association Rulesmentioning

confidence: 99%

Section: Triadic Association Rules Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mining Triadic Association Rules

Selmane

Missaoui²,

Boussaïd³

et al. 2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

Self Cite

View full text Add to dashboard Cite

show abstract

An Axiomatic System for Conditional Attribute Implications in Triadic Concept Analysis

Rodríguez‐Lorenzo

Enciso

Missaoui

et al. 2017

Int. J. Intell. Syst.

Self Cite

View full text Add to dashboard Cite

In this paper, we define a sound and complete inference system for triadic implications generated from a formal triadic context K:=⟨G,M,B,I⟩, where G, M, and B are object, attribute, and condition sets, respectively, and I is a ternary relation I⊆G×M×B. The inference system is expressed as a set of axioms “à la Armstrong.” The type of triadic implications we are considering in this paper is called conditional attribute implication (CAI) and has the following form: X→scriptCY, where X and Y are subsets of M, and scriptC is a subset of B. Such implication states that Ximplies Y under all conditions in scriptC and any subset of it. Moreover, we propose a method to compute CAIs from Biedermann's implications. We also introduce an algorithm to compute the closure of an attribute set X w.r.t. a set Σ of CAIs given a set scriptC of conditions.

show abstract

Knowledge representation and processing with formal concept analysis

Kuznetsov

Poelmans

2013

WIREs Data Min & Knowl

View full text Add to dashboard Cite

During the last three decades, formal concept analysis (FCA) became a well‐known formalism in data analysis and knowledge discovery because of its usefulness in important domains of knowledge discovery in databases (KDD) such as ontology engineering, association rule mining, machine learning, as well as relation to other established theories for representing knowledge processing, like description logics, conceptual graphs, and rough sets. In early days, FCA was sometimes misconceived as a static crisp hardly scalable formalism for binary data tables. In this paper, we will try to show that FCA actually provides support for processing large dynamical complex (may be uncertain) data augmented with additional knowledge. © 2013 Wiley Periodicals, Inc. This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence

show abstract

Mining Triadic Association Rules from Ternary Relations

Cited by 27 publications

References 14 publications

Mining Triadic Association Rules

Mining Triadic Association Rules

An Axiomatic System for Conditional Attribute Implications in Triadic Concept Analysis

Knowledge representation and processing with formal concept analysis

Contact Info

Product

Resources

About