Cluster Analysis of Medical Research Data using R

Lavanya Pamulaparty

Cluster Analysis of Medical Research Data using R

Lavanya Pamulaparty * §

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Osmania University

Cluster Analysis of Medical Research Data using R

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Abstract

Cluster analysis divides the data into groups that are meaningful, useful or both. It is also used as a starting point for other purposes of data summarization. This paper discuss some very basic algorithms like K-means, Fuzzy C-means, Hierarchical clustering to come up with clusters, and use R data mining tool. The results are tested on the datasets namely Online News Popularity, Iris Data Set and from UCI data repository and mi RNA dataset for medical data analysis. All datasets was analyzed with different clustering algorithms and the figures we are showing is the working of them in R data mining tool. Every algorithm has its uniqueness and antithetical behavior.

References

13 Cites in Article

Reference Format

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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

Lavanya Pamulaparty. 2016. "Cluster Analysis of Medical Research Data using R". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C1).

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GJCST Volume 16 Issue C1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-C Classification H.3.3 I.5.3

Submission ReceivedDecember 15, 2015
Peer Review Double Blind
Handling Editor
Accepted January 1, 2016
Published January 15, 2016

Version of record

v1.2

Issue date

April 22, 2016

Language

English

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Article Matrices

Total Score: 153

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Lavanya Pamulaparty, Dr. C. V Guru Rao, Dr. M. Sreenivasa Rao (PhD/Dr. count: 2)

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