FP-MAXFLOW: An Algorithm for Mining Maximum Relevant Patterns

Authors

  • Arnaldo Díaz Universidad de La Habana ICIDCA
  • Luciano García Universidad de La Habana

DOI:

https://doi.org/10.13053/cys-22-2-2498

Keywords:

Transactional databases, association mining, patterns recognition, frequent itemsets.

Abstract

Algorithms for itemsets recognition and classification have been widely studied. The state of the art shows that most of the algorithms obtain all possible itemsets, others only obtain maximal ones. Both approaches have limitations for getting relevant correlations. Obtaining all itemsets imply many irrelevant patterns. By obtaining only maximal patterns important information could be ignored. The goal of this work is to offer an algorithm for obtaining the patterns which more efficiently could describe the correlations. The new algorithm, called FP-MAXFLOW is able to extract these, information efficiently with one database scan. The comparative studies show that it is a competitive solution according to other algorithms which are among the most used.

Author Biographies

Arnaldo Díaz, Universidad de La Habana ICIDCA

Arnaldo Díaz Molina es Ingeniero de Software por el Instituto Superior Politécnico José Antonio Echeverría (ISPJAE, 2011), La Habana, Cuba. Sus áreas de interés son: Ingeniería de Software, Bases de Datos, Lógica Matemática, Minería de Datos, Aprendizaje de Computadoras e Inteligencia Artificial.

Luciano García, Universidad de La Habana

Luciano García Garrrido es Profesor Titular Consultante de la Facultad de Matemática y Computación de la Universidad de La Habana. Su área de investigación compende fundamentalmente los métodos de la lógica computacional y el aprendizaje de máquina  y sus aplicaciones en la extracción de información y la minería de textos.

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Published

2018-06-29

Issue

Vol. 22 No. 2 (2018): Thematic Issue on Numerical and Evolutionary Optimization (Guest editors: L. Trujillo, Y. Maldonado, O. Schuetze)

Section

Articles

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