Isotropic Dynamic Hierarchical Clustering

Victor Sadikov
Victor Sadikov
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Oliver Rutishauser
Oliver Rutishauser
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Isotropic Dynamic Hierarchical Clustering

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Abstract

We face a business need of discovering a pattern in locations of a great number of points in a high-dimensional space. We assume that there should be a certain structure, so that in some locations the points are close while in other locations the points are more dispersed. Our goal is to group the close points together. The process of grouping close objects is known under the name of clustering.

References

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  5. George Miller (2003). WordNet Lexical Database of English Language, Cognitive Science Laboratory of Princeton University 6. Roget's Thesaurus.
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Victor Sadikov. 2016. \u201cIsotropic Dynamic Hierarchical Clustering\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C3).

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  • Open Access
  • CC-BY-ND-NC 4.0
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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
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Classification
GJCST-C Classification H.3.3
I.5.3
  • Submission ReceivedDecember 12, 2015
  • Peer Review Double Blind
    Handling Editor
  • Accepted January 3, 2016
  • Published January 15, 2016
Version of record

v1.2

Issue date
July 1, 2016

Language
en
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High-quality academic research articles on global topics and journals.

Isotropic Dynamic Hierarchical Clustering

Victor Sadikov
Victor Sadikov
Oliver Rutishauser
Oliver Rutishauser
  • Submission ReceivedDecember 12, 2015
  • Peer Review Double Blind
    Handling Editor
  • Accepted January 3, 2016
  • Published January 15, 2016

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