Author:
Guillaume Petiot
Affiliation:
CERES, Catholic Institute of Toulouse, 31 rue de la Fonderie, Toulouse and France
Keyword(s):
Data Mining, Formal Concept Analysis, Possibility Theory, Uncertain Logical Gates.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Structured Data Analysis and Statistical Methods
;
Symbolic Systems
Abstract:
Formal Concept Analysis (FCA) is an approach of data mining which consists in extracting formal concepts in order to provide a hierarchy of concepts also called a concept lattice. It is useful for understanding data. A formal concept is a set of objects which share the same properties. When the number of formal concepts is too high, it is difficult to explore all formal concepts in order to look for information. The use of a query to extract relevant information is a solution to this problem. A logical combination of Boolean criteria, which can be represented by a logical circuit, can serve as the condition of the query. In the uncertain formal context, we are not sure if the objects own a property. As a consequence, we must take into account uncertainties in the computation of formal concepts and in queries. We propose in this paper to use possibility theory to handle these uncertainties. As a result, we compute a necessity degree for each formal concept. We can use a query in which
the condition can be computed by using possibilistic networks and uncertain logical gates. Finally, we illustrate our approach by the analysis of a satisfaction questionnaire for a course in bachelor.
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