A new clustering method using an augmentation to the self organizing maps

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)

1 Citation (Scopus)
3 Downloads (Pure)

Abstract

A technique is developed using Self Organizing Maps (SOM) to efficiently cluster the data and it is compared with existing clustering Techniques such as K-Means clustering, Hierarchical clustering and SOM Clustering. The proposed technique is used to cluster an Earthquake dataset and the performance is compared with the other existing clustering technique. The experimental results show that the proposed clustering method demonstrated better results as compared to other clustering methods.
Original languageEnglish
Title of host publicationNot Known
Pages739-743
DOIs
Publication statusE-pub ahead of print - 23 Aug 2018
Event8th International Conference on Cloud Computing, Data Science & Engineering (Confluence) - , India
Duration: 11 Jan 201812 Jan 2018

Conference

Conference8th International Conference on Cloud Computing, Data Science & Engineering (Confluence)
CountryIndia
Period11/01/1812/01/18

Fingerprint

Self organizing maps
Earthquakes

Keywords

  • Clustering
  • Self Organizing Map (SOM)
  • Hierarchical Clustering and K-means clustering.

Cite this

@inproceedings{47c92039cf5d4367a72e95ed23ead67a,
title = "A new clustering method using an augmentation to the self organizing maps",
abstract = "A technique is developed using Self Organizing Maps (SOM) to efficiently cluster the data and it is compared with existing clustering Techniques such as K-Means clustering, Hierarchical clustering and SOM Clustering. The proposed technique is used to cluster an Earthquake dataset and the performance is compared with the other existing clustering technique. The experimental results show that the proposed clustering method demonstrated better results as compared to other clustering methods.",
keywords = "Clustering, Self Organizing Map (SOM), Hierarchical Clustering and K-means clustering.",
author = "Hari Pandey",
year = "2018",
month = "8",
day = "23",
doi = "10.1109/CONFLUENCE.2018.8442431",
language = "English",
isbn = "978-1-5386-1719-9",
pages = "739--743",
booktitle = "Not Known",

}

Pandey, H 2018, A new clustering method using an augmentation to the self organizing maps. in Not Known. pp. 739-743, 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence), India, 11/01/18. https://doi.org/10.1109/CONFLUENCE.2018.8442431

A new clustering method using an augmentation to the self organizing maps. / Pandey, Hari.

Not Known. 2018. p. 739-743.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)

TY - GEN

T1 - A new clustering method using an augmentation to the self organizing maps

AU - Pandey, Hari

PY - 2018/8/23

Y1 - 2018/8/23

N2 - A technique is developed using Self Organizing Maps (SOM) to efficiently cluster the data and it is compared with existing clustering Techniques such as K-Means clustering, Hierarchical clustering and SOM Clustering. The proposed technique is used to cluster an Earthquake dataset and the performance is compared with the other existing clustering technique. The experimental results show that the proposed clustering method demonstrated better results as compared to other clustering methods.

AB - A technique is developed using Self Organizing Maps (SOM) to efficiently cluster the data and it is compared with existing clustering Techniques such as K-Means clustering, Hierarchical clustering and SOM Clustering. The proposed technique is used to cluster an Earthquake dataset and the performance is compared with the other existing clustering technique. The experimental results show that the proposed clustering method demonstrated better results as compared to other clustering methods.

KW - Clustering

KW - Self Organizing Map (SOM)

KW - Hierarchical Clustering and K-means clustering.

U2 - 10.1109/CONFLUENCE.2018.8442431

DO - 10.1109/CONFLUENCE.2018.8442431

M3 - Conference proceeding (ISBN)

SN - 978-1-5386-1719-9

SP - 739

EP - 743

BT - Not Known

ER -