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Communication Dans Un Congrès Année : 2009

Agglomerating Local Patterns Hierarchically with ALPHA

Pierre-Nicolas Mougel
  • Fonction : Auteur
  • PersonId : 1232070
  • IdHAL : pnmougel
Jean-François Boulicaut

Résumé

To increase the relevancy of local patterns discovered from noisy relations, it makes sense to formalize error-tolerance. Our starting point is to address the limitations of state-of-the-art methods for this purpose. Some extractors perform an exhaustive search w.r.t. a declarative specification of error-tolerance. Nevertheless, their computational complexity prevents the discovery of large relevant patterns. Alpha is a 3-step method that (1) computes complete collections of closed patterns, possibly error-tolerant ones, from arbitrary n-ary relations, (2) enlarges them by hierarchical agglomeration, and (3) selects the relevant agglomerated patterns.

Dates et versions

hal-01437766 , version 1 (17-01-2017)

Identifiants

Citer

Loïc Cerf, Pierre-Nicolas Mougel, Jean-François Boulicaut. Agglomerating Local Patterns Hierarchically with ALPHA. Proc. 18th ACM International Conference on Information and Knowledge Management CIKM'09, Nov 2009, Hong Kong, China. pp.1753-1756, ⟨10.1145/1645953.1646222⟩. ⟨hal-01437766⟩
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