Por favor utiliza este link para citar o compartir este documento: http://repositoriodigital.academica.mx/jspui/handle/987654321/8608
Título: Clustering based on rules and Knowledge Discovery in ill-structured domains
Clustering based on rules and Knowledge Discovery in ill-structured domains
Autores: 
Palabras clave: SOFTWARE ENGINEERING; SOFTWARE PROCESS; PHASE; ACTIVITY; PRODUCT; ROLE; MODEL OF OBJECTS
Fecha de publicación: 10-Sep-2011
Editorial: Computación y Sistemas
Descripción: IT IS CLEAR THAT NOWADAYS ANALYSIS OF COMPLEX SYSTEMS IS AN IMPORTANT HANDICAP FOR EITHER STATISTICS, ARTIFICIAL INTELIGENCE, INFORMATION SYSTEMS, DATA VISUALIZATION""| DESCRIBING THE STRUCTURE OR OBTAINING KNOWLWDGE FROM COMPLEX SYSTEMS IS KNOWN AS A DIFFICULT TASK. IT IS INEGABLE THAT THE COMBINATION OF DATA ANALYSIS TECHNIQUES (CLUSTERING AMONG THEM), INDUCTIVE LAERNING (KNOWLEDGE - BASED SYSTEMS), MANAGEMENT OF DATA BASES AND MULTIMENSIONAL GRAPHICAL REPRESENTATION MUST PRODUCE BENEFITS ON THIS LINE. FACTING THE AUTOMATED KNOWLEDGE DISCOVERY OF ILL - STRUCTURED DOMAINS RAISES SOME PROBLEMS EITHER FROM A MACHINE LEARNING OR CLUSTERING POINT OF VIEW. CLUSTERING BASED ON RULES (CBR) IS A METHODOLOGY DEVELOPED IN (9) WITH THE AIM OF FIDING THE STRUCTURE OF ILL - STRUCTURED DOMAINS. IN OUR PROPOSAL, A COMBINATION OF CLUSTERING AND INDUCTIVE LEARNING IS FOCUSSED TO THE PROBLEM OF FINDING AND INTERPRETING SPECIAL PATTERNS (OR CONCEPTS) FROM LARGE DATA BASES, IN ORDER TO EXTRACT USEFUL KNOWLEDGE TO REPRESENT REAL - WORD DOMAINS, GIVING BETTER PERFOMANCE THAN TRADITIONAL CLUSTERING ALGORITHMS OR KNOWLWDGE BASED SYSTEMS APPROACH. THE SCOPE OF THIS PAPER IS TO PRESENT THE METHODOLOGY ITSELF AS WELL AS TO SHOW HOW CBR HAS SEVERAL CONNECTIN POINTS WITH KNOWLEDGE DISCOVERY OF DATA. SOME APPLICATIONS ARE USED TO ILLUSTRATE THIS IDEAS.
IT IS CLEAR THAT NOWADAYS ANALYSIS OF COMPLEX SYSTEMS IS AN IMPORTANT HANDICAP FOR EITHER STATISTICS, ARTIFICIAL INTELIGENCE, INFORMATION SYSTEMS, DATA VISUALIZATION”¦ DESCRIBING THE STRUCTURE OR OBTAINING KNOWLWDGE FROM COMPLEX SYSTEMS IS KNOWN AS A DIFFICULT TASK. IT IS INEGABLE THAT THE COMBINATION OF DATA ANALYSIS TECHNIQUES (CLUSTERING AMONG THEM), INDUCTIVE LAERNING (KNOWLEDGE - BASED SYSTEMS), MANAGEMENT OF DATA BASES AND MULTIMENSIONAL GRAPHICAL REPRESENTATION MUST PRODUCE BENEFITS ON THIS LINE. FACTING THE AUTOMATED KNOWLEDGE DISCOVERY OF ILL - STRUCTURED DOMAINS RAISES SOME PROBLEMS EITHER FROM A MACHINE LEARNING OR CLUSTERING POINT OF VIEW. CLUSTERING BASED ON RULES (CBR) IS A METHODOLOGY DEVELOPED IN (9) WITH THE AIM OF FIDING THE STRUCTURE OF ILL - STRUCTURED DOMAINS. IN OUR PROPOSAL, A COMBINATION OF CLUSTERING AND INDUCTIVE LEARNING IS FOCUSSED TO THE PROBLEM OF FINDING AND INTERPRETING SPECIAL PATTERNS (OR CONCEPTS) FROM LARGE DATA BASES, IN ORDER TO EXTRACT USEFUL KNOWLEDGE TO REPRESENT REAL - WORD DOMAINS, GIVING BETTER PERFOMANCE THAN TRADITIONAL CLUSTERING ALGORITHMS OR KNOWLWDGE BASED SYSTEMS APPROACH. THE SCOPE OF THIS PAPER IS TO PRESENT THE METHODOLOGY ITSELF AS WELL AS TO SHOW HOW CBR HAS SEVERAL CONNECTIN POINTS WITH KNOWLEDGE DISCOVERY OF DATA. SOME APPLICATIONS ARE USED TO ILLUSTRATE THIS IDEAS.
Other Identifiers: http://revistas.unam.mx/index.php/cys/article/view/2462
Aparece en las Colecciones:Computación y Sistemas

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