HCuRMD: Hierarchical Clustering Using Relative Minimal Distances - Artificial Intelligence Applications and Innovations (AIAI 2015) Access content directly
Conference Papers Year : 2015

HCuRMD: Hierarchical Clustering Using Relative Minimal Distances

Charalampos Goulas
  • Function : Author
  • PersonId : 991100
Dimitrios Chondrogiannis
  • Function : Author
  • PersonId : 991101
Theodoros Xenakis
  • Function : Author
  • PersonId : 991102
Alexandros Xenakis
  • Function : Author
  • PersonId : 991103
Photis Nanopoulos
  • Function : Author
  • PersonId : 991104

Abstract

In recent years, the ever increasing production of huge amounts of data has led the research community into trying to find new machine learning techniques in order to gain insight and discover hidden structures and correlation among these data. Therefore, clustering has become one of the most widely used techniques for exploratory data analysis. In this sense, this paper is proposing a new approach in hierarchical clustering; named HCuRMD, which improves the overall complexity of the whole clustering process by using a more relative perspective in defining minimal distances among different objects.
Fichier principal
Vignette du fichier
978-3-319-23868-5_32_Chapter.pdf (303.91 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01385380 , version 1 (21-10-2016)

Licence

Attribution

Identifiers

Cite

Charalampos Goulas, Dimitrios Chondrogiannis, Theodoros Xenakis, Alexandros Xenakis, Photis Nanopoulos. HCuRMD: Hierarchical Clustering Using Relative Minimal Distances. 11th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2015), Sep 2015, Bayonne, France. pp.440-447, ⟨10.1007/978-3-319-23868-5_32⟩. ⟨hal-01385380⟩
78 View
100 Download

Altmetric

Share

Gmail Facebook X LinkedIn More