Título
Mining frequent patterns and association rules using similarities
Autor
ANSEL YOAN RODRIGUEZ GONZALEZ
José Francisco Martínez Trinidad
Jesús Ariel Carrasco Ochoa
Nivel de Acceso
Acceso Abierto
Materias
Data mining - (DATA MINING) Frequent patterns - (FREQUENT PATTERNS) Association rules - (ASSOCIATION RULES) Mixed data - (MIXED DATA) Similarity functions - (SIMILARITY FUNCTIONS) Downward closure property - (DOWNWARD CLOSURE PROPERTY) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)
Resumen o descripción
Most of the current algorithms for mining association rules assume that two object subdescriptions are similar when they are exactly equal, but in many real world problems some other similarity functions are used. Commonly these algorithms are divided in two steps: Frequent pattern mining and generation of interesting association rules from frequent patterns. In this work, two algorithms for mining frequent similar patterns using similarity functions different from the equality are proposed. Additionally, the Gen- Rules Algorithm is adapted to generate interesting association rules from frequent similar patterns. Experimental results show that our algorithms are more effective and obtain better quality patterns than the existing ones.
Editor
Elsevier Ltd.
Fecha de publicación
2013
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Estudiantes
Investigadores
Público en general
Sugerencia de citación
Rodríguez, A., et al., (2013). Mining frequent patterns and association rules using similarities, Expert Systems with Applications, Vol. 2013 (40): 6823-6836
Repositorio Orígen
Repositorio Institucional del INAOE
Descargas
102