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Criteria for building concepts in CCEC

Criteria for building concepts in CCEC . Karina Gibert 1 , Alejandra Pérez-Bonilla 1 , Darko Vrecko 2 1 Department of Statistics and Operations Research.Technical University of Catalonia. 2 Department of Systems and Control. Jozef Stefan Institute. CCEC overview. P(r)=1. P(r)=1. KB. KB.

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Criteria for building concepts in CCEC

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  1. Criteria for building concepts in CCEC Karina Gibert1, Alejandra Pérez-Bonilla1, Darko Vrecko2 1Department of Statistics and Operations Research.Technical University of Catalonia. 2Department of Systems and Control. Jozef Stefan Institute

  2. CCEC overview P(r)=1 P(r)=1 KB KB KB CWA Chaining Concepts: • Best Global concept and Close-World Assumption C1: A11^A12^A13 C2: not C1 RelCov=100% RelCov=100% r:A2→C1 Máx Confidence MáxRelat. Covering r:A3→C1 r:A1→C1 C1: A11 ٧A12 ٧A13 C2: not C1

  3. CCEC overview P(r)=1 P(r)=1 P(r)=1 P(r)=1 KB KB KB KB KB No CWA Chaining Concepts: • Best local concept and no Close-WorldAssumption C1: A11^A12^A13 RelCov=100% RelCov=100% RelCov=100% RelCov=100% C1 r:A2→C1 r:A2→C1 C1: A11 ٧A12 ٧A13 Split by consequent Máx Confidence Máx Confidence MáxRelat. Covering MáxRelat. Covering r:A3→C1 r:A3→C1 C2: A21 ^A22 ^A23 r:A1→C1 r:A1→C1 C2 C2: A21 ٧A22 ٧A23

  4. CCEC overview P(r)=1 P(r)=1 P(r)=1 P(r)=1 KB KB KB KB KB CWA Chaining Concepts: • Best local concept and Close-WorldAssumption C1: A11^A12^A13 ٧ ̚C2 RelCov=100% RelCov=100% RelCov=100% RelCov=100% C1 r:A2→C1 r:A2→C1 C1: A11 ٧A12 ٧A13 ٧ ̚C2 Split by consequent Máx Confidence Máx Confidence MáxRelat. Covering MáxRelat. Covering r:A3→C1 r:A3→C1 C2: A21 ^A22 ^A23 ٧ ̚C1 r:A1→C1 r:A1→C1 C2 C2: A21 ٧A22 ٧A23 ٧ ̚C1

  5. CCEC overview P(r)=1 P(r)=1 P(r)=1 P(r)=1 KB KB KB KB KB Partial CWA Chaining Concepts: • Best local concept and partialClose-WorldAssumption C1: A11^A12^A13 ٧ ̚C2 Onlyfor variables notincluded in C1 RelCov=100% RelCov=100% RelCov=100% RelCov=100% C1 r:A2→C1 r:A2→C1 C1: A11 ٧A12 ٧A13 ٧ ̚C2 Split by consequent Máx Confidence Máx Confidence MáxRelat. Covering MáxRelat. Covering r:A3→C1 r:A3→C1 C2: A21 ^A22 ^A23 ٧ ̚C1 r:A1→C1 r:A1→C1 Onlyfor variables notincluded in C2 C2 C2: A21 ٧A22 ٧A23 ٧ ̚C1

  6. CCEC overview P(r)=1 P(r)=1 P(r)=1 P(r)=1 KB KB KB KB KB Partial CWA Chaining Concepts: • Bestlocal-global concept and partialClose-WorldAssumption C1: A11^A12^A13 ٧ ̚C2 Onlyfor variables notincluded in C1 RelCov=100% RelCov=100% RelCov=100% RelCov=100% C1 r:A2→C1 r:A2→C1 C1: A11 ٧A12 ٧A13 ٧ ̚C2 Split by consequent Máx Confidence Máx Confidence MáxRelat. Covering MáxRelat. Covering r:A3→C1 r:A3→C1 C2: A21 ^A22 ^A23 ٧ ̚C1 r:A1→C1 r:A1→C1 Onlyfor variables notincluded in C2 C2 Global strategyforrepeated variables: Substituteby A12 whenhigherrelCov C2: A21 ٧A22 ٧A23 ٧ ̚C1

  7. Is there any question?... Automatic generation of conceptual descriptionsof classifications in Environmental Domains K. Gibert1, A. Pérez-Bonilla1, D. Vrecko2 1Department of Statistics and Operations Research.Technical University of Catalonia. 2Department of Systems and Control. Jozef Stefan Institute International Congress on Environmental Modelling and Software iEMSs 2008July 7-10, 2008 - Barcelona, Catalonia

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