XenakisV1, Main, Exploration, bibRecord, 000015

HCuRMD: Hierarchical Clustering Using Relative Minimal Distances

Identifieur interne : 000015 ( Main/Exploration ); précédent : 000014; suivant : 000016

HCuRMD: Hierarchical Clustering Using Relative Minimal Distances

Auteurs : Charalampos Goulas [Grèce] ; Dimitrios Chondrogiannis [Grèce] ; Theodoros Xenakis [Grèce] ; Alexandros Xenakis [Grèce] ; Photis Nanopoulos [Grèce]

Source :

RBID : Hal:hal-01385380

English descriptors

mix :
- Artificial Intelligence, Hierarchical Clustering, Machine Learning, Relative Minimal Distances.

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.

Url:

https://hal.inria.fr/hal-01385380

DOI: 10.1007/978-3-319-23868-5_32

Affiliations:

Grèce

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 000013
to stream Hal, to step Curation: 000013
to stream Hal, to step Checkpoint: 000006
to stream Main, to step Merge: 000016
to stream Main, to step Curation: 000015

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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HCuRMD: Hierarchical Clustering Using Relative Minimal Distances

HCuRMD: Hierarchical Clustering Using Relative Minimal Distances

Source :

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri