Specifying Mining Algorithms with Iterative User-Defined Aggregates (Articolo in rivista)

Type
Label
  • Specifying Mining Algorithms with Iterative User-Defined Aggregates (Articolo in rivista) (literal)
Anno
  • 2004-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/TKDE.2004.64 (literal)
Alternative label
  • Giannotti F., Manco G., Turini F. (2004)
    Specifying Mining Algorithms with Iterative User-Defined Aggregates
    in IEEE transactions on knowledge and data engineering (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Giannotti F., Manco G., Turini F. (literal)
Pagina inizio
  • 1232 (literal)
Pagina fine
  • 1246 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 16 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • Vol. 16 n.10, pp. 1232-1246. IEEE, 2004. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 15 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 10 (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Google Scholar (literal)
  • Scopus (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ISTI-CNR Pisa, Dipartimento di Informatica, Università di Pisa (literal)
Titolo
  • Specifying Mining Algorithms with Iterative User-Defined Aggregates (literal)
Abstract
  • We present a way of exploiting domain knowledge in the design and implementation of data mining algorithms, with special attention to frequent patterns discovery, within a deductive framework. In our framework, domain knowledge is represented by way of deductive rules, and data mining algorithms are specified by means of iterative user-defined aggregates and implemented by means of user-defined predicates. This choice allows us to exploit the full expressive power of deductive rules without loosing in performance. Iterative user-defined aggregates have a fixed scheme, in which user-defined predicates are to be added. This feature allows the modularization of data mining algorithms, thus providing a way to integrate the proper domain knowledge exploitation in the right point. As a case study, the paper presents how user-defined aggregates can be exploited to specify and implement a version of the a priori algorithm. Some performance analyzes and comparisons are discussed in order to show the effectiveness of the approach. (literal)
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