Mining top-K patterns from binary datasets in presence of noise (Contributo in atti di convegno)

Type

• Prodotto della ricerca (Classe)
• Contributo in atti di convegno (Classe)

Label

• Mining top-K patterns from binary datasets in presence of noise (Contributo in atti di convegno) (literal)

Anno

• 2010-01-01T00:00:00+01:00 (literal)

Alternative label

• Lucchese C.; Orlando S.; Perego R. (2010)
  Mining top-K patterns from binary datasets in presence of noise
  in Tenth SIAM International Conference on Data Mining, Columbus, Ohio, US
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Lucchese C.; Orlando S.; Perego R. (literal)

Pagina inizio

• 165 (literal)

Pagina fine

• 176 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://www.siam.org/proceedings/datamining/2010/dm10_015_lucchesec.pdf (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

• In: SDM10 - Tenth SIAM International Conference on Data Mining (Columbus, Ohio, US, April 29 - May 1 2010). Proceedings, pp. 165 - 176. SIAM, 2010. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto

• ABSTRACT: The discovery of patterns in binary dataset has many ap- plications, e.g. in electronic commerce, TCP/IP networking, Web usage logging, etc. Still, this is a very challenging task in many respects: overlapping vs. non overlapping patterns, presence of noise, extraction of the most important patterns only. In this paper we formalize the problem of discovering the Top-K patterns from binary datasets in presence of noise, as the minimization of a novel cost function. According to the Minimum Description Length principle, the proposed cost function favors succinct pattern sets that may approximately describe the input data. We propose a greedy algorithm for the discovery of Patterns in Noisy Datasets, named PaNDa, and show that it outperforms related techniques on both synthetic and real- world data. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• CNR-ISTI, Pisa, Dipartimento di Informatica, Università Ca' Foscari di Venezia, CNR-ISTI, Pisa (literal)

Titolo

• Mining top-K patterns from binary datasets in presence of noise (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

• 978-0-898717-03-7 (literal)

Abstract

• The discovery of patterns in binary dataset has many applications, e.g. in electronic commerce, TCP/IP networking, Web usage logging, etc. Still, this is a very challenging task in many respects: overlapping vs. non overlapping patterns, presence of noise, extraction of the most important patterns only. In this paper we formalize the problem of discovering the Top-K patterns from binary datasets in presence of noise, as the minimization of a novel cost function. According to the Minimum Description Length principle, the proposed cost function favors succinct pattern sets that may approximately describe the input data. We propose a greedy algorithm for the discovery of Patterns in Noisy Datasets, named PaNDa, and show that it outperforms related techniques on both synthetic and realworld data. (literal)

Prodotto di

• Sistemi e algoritmi per Big Data (ICT.P09.006.001) (Modulo)
• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR

• CLAUDIO LUCCHESE (Persona)
• RAFFAELE PEREGO (Unità di personale interno)

Insieme di parole chiave

• Parole chiave di "Mining top-K patterns from binary datasets in presence of noise" (Insieme di parole chiave)

Incoming links:

Autore CNR di

• RAFFAELE PEREGO (Unità di personale interno)
• CLAUDIO LUCCHESE (Persona)

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Sistemi e algoritmi per Big Data (ICT.P09.006.001) (Modulo)

Insieme di parole chiave di

• Parole chiave di "Mining top-K patterns from binary datasets in presence of noise" (Insieme di parole chiave)