Efficient mining of association rules using closed itemset lattices
, , , und .
Information Systems 24 (1): 25 - 46 (1999)

Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a sub-order of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data.
  • @stumme
  • @stephandoerfel
Diese Publikation wurde noch nicht bewertet.

Durchschnittliche Benutzerbewertung0,0 von 5.0 auf Grundlage von 0 Rezensionen
    Bitte melden Sie sich an um selbst Rezensionen oder Kommentare zu erstellen.