• xmlui.mirage2.page-structure.header.title
    • français
    • English
  • Help
  • Login
  • Language 
    • Français
    • English
View Item 
  •   BIRD Home
  • LAMSADE (UMR CNRS 7243)
  • LAMSADE : Publications
  • View Item
  •   BIRD Home
  • LAMSADE (UMR CNRS 7243)
  • LAMSADE : Publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesTypeThis CollectionBy Issue DateAuthorsTitlesType

My Account

LoginRegister

Statistics

Most Popular ItemsStatistics by CountryMost Popular Authors
Thumbnail

Extension of Partitional Clustering Methods for Handling Mixed Data

Naija, Yosr; Chakhar, Salem; Blibech, Kaouther; Robbana, Riadh (2008), Extension of Partitional Clustering Methods for Handling Mixed Data, IEEE International Conference on Data Mining Workshops, 2008. ICDMW '08. Proceedings, IEEE, p. 257-266. http://dx.doi.org/10.1109/ICDMW.2008.85

View/Open
NCBR-MCD2008.pdf (148.9Kb)
Type
Communication / Conférence
Date
2008
Conference title
IEEE International Conference on Data Mining Workshops, 2008. ICDMW '08
Conference date
2008-12
Conference city
Pise
Conference country
Italie
Book title
IEEE International Conference on Data Mining Workshops, 2008. ICDMW '08. Proceedings
Publisher
IEEE
ISBN
978-0-7695-3503-6
Pages
257-266
Publication identifier
http://dx.doi.org/10.1109/ICDMW.2008.85
Metadata
Show full item record
Author(s)
Naija, Yosr
Chakhar, Salem
Blibech, Kaouther
Robbana, Riadh
Abstract (EN)
Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on the use of a distance measure defined either on numerical attributes or on categorical attributes. However, in fields such as road traffic and medicine, datasets are composed of numerical and categorical attributes. Recently, there have been several proposals to develop clustering methods that support mixed attributes. There are three basic categories of clustering methods: partitional methods, hierarchical methods and density-based methods. This paper proposes an extension of partitional clustering methods devoted to mixed attributes. The proposed extension looks to create several partitions by using numerical attributes-based clustering methods and then chooses the one that maximizes a measure---called ``homogeneity degree"---of these partitions according to categorical attributes.
Subjects / Keywords
mixed data; homogeneity degree; Pratitional clustering

Related items

Showing items related by title and author.

  • Thumbnail
    Sparse and group-sparse clustering for mixed data An illustration of the vimpclust package 
    Chavent, Marie; Lacaille, Jérôme; Mourer, Alex; Olteanu, Madalina (2022) Communication / Conférence
  • Thumbnail
    Sparse k-means for mixed data via group-sparse clustering 
    Chavent, Marie; Lacaille, Jerome; Mourer, Alex; Olteanu, Madalina (2020) Communication / Conférence
  • Thumbnail
    Data fusion application from evidential databases as a support for decision making 
    Telmoudi, Abdelkader; Chakhar, Salem (2004) Article accepté pour publication ou publié
  • Thumbnail
    Implementing imperfect information in fuzzy databases 
    Bahri, Afef; Chakhar, Salem; Naïja, Yosr; Bouaziz, Rafik (2005) Communication / Conférence
  • Thumbnail
    Co-clustering based exploratory analysis of mixed-type data tables 
    Bouchareb, Aichetou; Boullé, Marc; Clérot, Fabrice; Rossi, Fabrice (2019) Chapitre d'ouvrage
Dauphine PSL Bibliothèque logo
Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16
Phone: 01 44 05 40 94
Contact
Dauphine PSL logoEQUIS logoCreative Commons logo