Article

F2G: efficient discovery of full-patterns

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

Henriques, Rui; Madeira, Sara C; Antunes, Cláudia — 2013

Key information

Authors:

Henriques, Rui (Rui Miguel Carrasqueiro Henriques); Madeira, Sara C (Sara Alexandra Cordeiro Madeira); Antunes, Cláudia (Claudia Martins Antunes)

Published in

September 2013

Abstract

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.

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Publication version

AO - Author's Original

Title of the publication container

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

Fields of Science and Technology (FOS)

computer-and-information-sciences - Computer and information sciences

Publication language (ISO code)

eng - English

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