Clustering on Spatial Data Sets Using Extended Linked Clustering Algorithms

K Lakshmaiah

Clustering on Spatial Data Sets Using Extended Linked Clustering Algorithms

K Lakshmaiah * §

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§ Sir Vishveshwaraiah Institute of Science & Technology

Clustering on Spatial Data Sets Using Extended Linked Clustering Algorithms

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Abstract

Various clustering algorithms (CA) have been reported in literature, to group data into clusters in diverse domains. Literature further reported that, these CAs work satisfactorily either on pure numerical data or on pure categorical data and perform poorly on mixed numerical and categorical data. Clustering is the process of creating distribution patterns and obtaining intrinsic correlations in large datasets by arranging the data into similarity classes. The present work pertains to reviewing the available research papers on clustering spatial data. In a web perspective, a detailed inspection of grouped patterns and their belonging to well known characters will be very useful for evolution of clusters. The review work is split into spatial data mining, clustering on spatial data sets and extended linked clustering. This review work will enable the researchers to make an in depth study of the till date research work on above areas and will pave way for developing extended linked clustering algorithms with a view to find number of clusters on mixed datasets to produce results for several datasets.

References

21 Cites in Article

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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

K Lakshmaiah. 2018. "Clustering on Spatial Data Sets Using Extended Linked Clustering Algorithms". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 18 (GJCST Volume 18 Issue C3).

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GJCST Volume 18 Issue C3

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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 I.5.3

Submission ReceivedDecember 13, 2017
Peer Review Double Blind
Handling Editor
Accepted December 31, 2017
Published July 27, 2018

Version of record

v1.2

Issue date

July 27, 2018

Language

English

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Article Matrices

Total Score: 101

Country: India

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

Authors: K Lakshmaiah (PhD/Dr. count: 0)

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Publish Date: 2018 01, Mon

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