F2G: efficient discovery of full-patterns

Setembro 2013

An increasing number of biomedical tasks, such as patternbased biclustering, require the disclosure of the transactions (e.g. genes) that support each pattern (e.g. expression profiles). The discovery of patterns with their supporting transactions, referred as full-pattern mining, has been solved recurring to extensions over Apriori and vertical-based algorithms for frequent itemset mining. Although pattern-growth alternatives are known to be more efficient across multiple biological datasets, there are not yet adaptations for the efficient delivery of full-patterns. In this paper, we propose a pattern-growth algorithm able to discover full-patterns with heightened efficiency and minimum memory overhead. Results confirm that for dense datasets or low support thresholds, a common requirement in biomedical settings, this method can achieve significant performance improvements against its peers.

Autores da comunidade :

• 

  R

  Rui Miguel Carrasqueiro Henriques

  ist156846

  Afiliações e fontes

  IST > INESC-ID

  Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento em Lisboa

  IST > DEI

  Departamento de Engenharia Informática

• 

  S

  Sara Alexandra Cordeiro Madeira

  ist46399

  Afiliações e fontes

  IST > INESC-ID

  Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento em Lisboa

• 

  C

  Claudia Martins Antunes

  ist14046

  Afiliações e fontes

  IST > DEI

  Departamento de Engenharia Informática

AO - Versão original do autor

ECML/PKDD Workshop on New Frontiers on Mining Complex Patterns at International Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2013)

computer-and-information-sciences - Ciências da Computação e da Informação

eng - Inglês