• English
    • Ελληνικά
    • Deutsch
    • français
    • italiano
    • español
  • français 
    • English
    • Ελληνικά
    • Deutsch
    • français
    • italiano
    • español
  • Ouvrir une session
Voir le document 
  •   Accueil de DSpace
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
  • Voir le document
  •   Accueil de DSpace
  • Επιστημονικές Δημοσιεύσεις Μελών ΠΘ (ΕΔΠΘ)
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ.
  • Voir le document
JavaScript is disabled for your browser. Some features of this site may not work without it.
Tout DSpace
  • Communautés & Collections
  • Par date de publication
  • Auteurs
  • Titres
  • Sujets

Clustering of high dimensional data streams

Thumbnail
Auteur
Tasoulis, S. K.; Tasoulis, D. K.; Plagianakos, V. P.
Date
2012
DOI
10.1007/978-3-642-30448-4_28
Sujet
Clustering
Data Streams
Incremental Principal Component Analysis
Kernel Density Estimation
Clustering approach
Data format
Data stream
High dimensional data
High dimensionality
High-dimensional
High-dimensional clustering
Streaming data
Artificial intelligence
Clustering algorithms
Data communication systems
Principal component analysis
Data mining
Afficher la notice complète
Résumé
Clustering of data streams has become a task of great interest in the recent years as such data formats is are becoming increasingly ambiguous. In many cases, these data are also high dimensional and in result more complex for clustering. As such there is a growing need for algorithms that can be applied on streaming data and the at same time can cope with high dimensionality. To this end, here we design a streaming clustering approach by extending a recently proposed high dimensional clustering algorithm. © 2012 Springer-Verlag.
URI
http://hdl.handle.net/11615/33580
Collections
  • Δημοσιεύσεις σε περιοδικά, συνέδρια, κεφάλαια βιβλίων κλπ. [19743]

Related items

Showing items related by title, author, creator and subject.

  • Thumbnail

    Nonlinear dimensionality reduction for clustering 

    Tasoulis S., Pavlidis N.G., Roos T. (2020)
    We introduce an approach to divisive hierarchical clustering that is capable of identifying clusters in nonlinear manifolds. This approach uses the isometric mapping (Isomap) to recursively embed (subsets of) the data in ...
  • Thumbnail

    Improving Hierarchical Short Text Clustering through Dominant Feature Learning 

    Akritidis L., Alamaniotis M., Fevgas A., Tsompanopoulou P., Bozanis P. (2022)
    This paper focuses on the popular problem of short text clustering. Since the short text documents typically exhibit high degrees of data sparseness and dimensionality, the problem in question is generally considered more ...
  • Thumbnail

    Enhancing Clustering of Single-Cell RNA-Seq Data by Proximity Learning on Random Projected Spaces 

    Vrahatis A.G., Dimitrakopoulos G.N., Tasoulis S.K., Plagianakos V.P. (2019)
    We are in the era of single-cell RNA sequencing technology, which offers a great potential for uncovering cellular differences with a higher resolution, shedding light in various complex biological processes and complex ...
htmlmap 

 

Parcourir

Tout DSpaceCommunautés & CollectionsPar date de publicationAuteursTitresSujetsCette collectionPar date de publicationAuteursTitresSujets

Mon compte

Ouvrir une sessionS'inscrire
Help Contact
DepositionAboutHelpContactez-nous
Choose LanguageTout DSpace
EnglishΕλληνικά
htmlmap