Author:
Guillaume Petiot
Affiliation:
CERES, Catholic Institute of Toulouse, 31 rue de la Fonderie, Toulouse and France
Keyword(s):
Formal Concept Analysis, Possibility Theory, Natural Language Processing, Neural Network, Data Mining.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Uncertainty in AI
Abstract:
The Formal Concept Analysis (FCA) is a method of data analysis often used in data mining. This method proposes to build a collection of formal concepts from a set of objects and their properties. These formal concepts can be ordered to provide a concept lattice. Several researches have demonstrated a link between the possibility theory and the formal concept analysis. Thus, it is possible to take into account the uncertainties of the properties by using the possibility theory before propagating them during the computation of the formal concepts. We propose in this paper an experimentation of the uncertain formal concept analysis for the extraction of knowledge in a satisfaction questionnaire for a course of professionalization in bachelor. Some questions can be open questions where the answers provided by students are given freely. For this purpose, we perform a text mining processing in order to categorize and classify the answers. During this processing, uncertainties can appear. I
n this research, we will handle these uncertainties by using the uncertain formal concept analysis. Then, we will extract the uncertain formal concepts from the concept lattice by using queries and represent the reduced lattice concepts with a visualization tool.
(More)