Please use this identifier to cite or link to this item: http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2391
Mining frequent patterns and association rules using similarities
ANSEL YOAN RODRIGUEZ GONZALEZ
José Francisco Martínez Trinidad
Jesús Ariel Carrasco Ochoa
Acceso Abierto
Atribución-NoComercial-SinDerivadas
Data mining
Frequent patterns
Association rules
Mixed data
Similarity functions
Downward closure property
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.
Elsevier Ltd.
2013
Artículo
Inglés
Estudiantes
Investigadores
Público en general
Rodríguez, A., et al., (2013). Mining frequent patterns and association rules using similarities, Expert Systems with Applications, Vol. 2013 (40): 6823-6836
CIENCIA DE LOS ORDENADORES
Versión aceptada
acceptedVersion - Versión aceptada
Appears in Collections:Artículos de Ciencias Computacionales

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