Isotropic Dynamic Hierarchical Clustering

Isotropic Dynamic Hierarchical Clustering

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Victor Sadikov

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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

7 Cites in Article

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Brian Goetz,' (2003). HTML Parser.

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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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GJCST Volume 16 Issue C3
Pg. 29- 36

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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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Article Matrices

Total Score: 132

Country: United States

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Victor Sadikov, Oliver Rutishauser (PhD/Dr. count: 0)

View Count (all-time): 294

Total Views (Real + Logic): 7414

Total Downloads (simulated): 2024

Publish Date: 2016 07, Fri

Monthly Totals (Real + Logic):

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Total Views: 7414

Total Downloads: 2024

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Published Article

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.

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