3,122 research outputs found
Visualizing and Interacting with Concept Hierarchies
Concept Hierarchies and Formal Concept Analysis are theoretically well
grounded and largely experimented methods. They rely on line diagrams called
Galois lattices for visualizing and analysing object-attribute sets. Galois
lattices are visually seducing and conceptually rich for experts. However they
present important drawbacks due to their concept oriented overall structure:
analysing what they show is difficult for non experts, navigation is
cumbersome, interaction is poor, and scalability is a deep bottleneck for
visual interpretation even for experts. In this paper we introduce semantic
probes as a means to overcome many of these problems and extend usability and
application possibilities of traditional FCA visualization methods. Semantic
probes are visual user centred objects which extract and organize reduced
Galois sub-hierarchies. They are simpler, clearer, and they provide a better
navigation support through a rich set of interaction possibilities. Since probe
driven sub-hierarchies are limited to users focus, scalability is under control
and interpretation is facilitated. After some successful experiments, several
applications are being developed with the remaining problem of finding a
compromise between simplicity and conceptual expressivity
CAISL: Simplification Logic for Conditional Attribute Implications
In this work, we present a sound and complete axiomatic system for conditional attribute implications (CAIs) in Triadic Concept Analysis (TCA). Our approach is strongly based on the Simplification paradigm which offers a more suitable way for automated reasoning than the one based on Armstrong’s Axioms. We also present an automated method to prove the derivability of a CAI from a set of CAI s.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Interpretation of Fuzzy Attribute Subsets in Generalized One-Sided Concept Lattices
In this paper we describe possible interpretation and reduction of fuzzy attributes in Generalized One-sided Concept Lattices (GOSCL). This type of concept lattices represent generalization of Formal Concept Analysis (FCA) suitable for analysis of datatables with different types of attributes. FCA as well as generalized one-sided concept lattices represent conceptual data miningmethods. With growing number of attributes the interpretation of fuzzy subsets may become unclear, hence another interpretation of this fuzzy attribute subsets can be valuable. The originality of the presented method is based on the usage of one-sided concept lattices derived from submodels of former object-attribute model by grouping attributes with the same truth value structure. This leads to new method for attribute reduction in GOSCL environment
Discovery of the D-basis in binary tables based on hypergraph dualization
Discovery of (strong) association rules, or implications, is an important
task in data management, and it nds application in arti cial intelligence,
data mining and the semantic web. We introduce a novel approach
for the discovery of a speci c set of implications, called the D-basis, that provides
a representation for a reduced binary table, based on the structure of
its Galois lattice. At the core of the method are the D-relation de ned in
the lattice theory framework, and the hypergraph dualization algorithm that
allows us to e ectively produce the set of transversals for a given Sperner hypergraph.
The latter algorithm, rst developed by specialists from Rutgers
Center for Operations Research, has already found numerous applications in
solving optimization problems in data base theory, arti cial intelligence and
game theory. One application of the method is for analysis of gene expression
data related to a particular phenotypic variable, and some initial testing is
done for the data provided by the University of Hawaii Cancer Cente
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