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Knowledge Discovery in Complex Objects
Author(s) -
Faïd Mourad,
Missaoui Rokia,
Godin Robert
Publication year - 1999
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/0824-7935.00080
Subject(s) - concatenation (mathematics) , computer science , knowledge extraction , exploit , formal concept analysis , process (computing) , artificial intelligence , set (abstract data type) , interpretation (philosophy) , theoretical computer science , natural language processing , mathematics , algorithm , programming language , computer security , combinatorics
Learning concepts and rules from structured (complex) objects is a quite challenging but very relevant problem in the area of machine learning and knowledge discovery. In order to take into account and exploit the semantic relationships that hold between atomic components of structured objects, we propose a knowledge discovery process, which starts from a set of complex objects to produce a set of related atomic objects (called contexts). The second step of the process makes use of the concatenation product to get a global context in which binary relations of individual contexts coexist with relations produced by the application of some operators to individual contexts. The last step permits the discovery of concepts and implication rules using the concept lattice as a framework in order to discover and interpret nontrivial concepts and rules that may relate different components of complex objects. This paper focuses on two main steps of the knowledge discovery process, namely data mining and interpretation.