Clustering Transactional Data (Contributo in atti di convegno)

Type
Label
  • Clustering Transactional Data (Contributo in atti di convegno) (literal)
Anno
  • 2002-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/3-540-45681-3_15 (literal)
Alternative label
  • Giannotti F.; Gozzi C.; Manco G. (2002)
    Clustering Transactional Data
    in 6th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2002, Helsinki, Finland, 19 August 2002 through 23 August 2002
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Giannotti F.; Gozzi C.; Manco G. (literal)
Pagina inizio
  • 175 (literal)
Pagina fine
  • 187 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 2431 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • SEBD 2001 (Sistemi Evoluti per Basi di Dati) - 9th Italian Symposium on Advanced Database Systems, Venezia. pp. 163-177 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ISTI-CNR,ISTI-CNR,ICAR-CNR (literal)
Titolo
  • Clustering Transactional Data (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-540-44037-6 (literal)
Abstract
  • In this paper we present a partitioning method capable to manage transactions, namelyt uples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the KMeans algorithm to represent dissimilarityam ong transactions, and redefine the notion of cluster centroid. The cluster centroid is used as the representative of the common properties of cluster elements. We show that using our concept of cluster centroid together with Jaccard distance we obtain results that are comparable in qualityw ith the most used transactional clustering approaches, but substantiallyi mprove their efficiency. (literal)
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