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

Hari Pandey

doi:10.1109/CONFLUENCE.2018.8442431

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

Hari Pandey

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding (ISBN) › peer-review

7 Citations (Scopus)

95 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 language	English
Title of host publication	Not Known
Pages	739-743
DOIs	https://doi.org/10.1109/CONFLUENCE.2018.8442431
Publication status	E-pub ahead of print - 23 Aug 2018
Event	8th International Conference on Cloud Computing, Data Science & Engineering (Confluence) - , India Duration: 11 Jan 2018 → 12 Jan 2018

Conference

Conference	8th International Conference on Cloud Computing, Data Science & Engineering (Confluence)
Country/Territory	India
Period	11/01/18 → 12/01/18

Keywords

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

Access to Document

10.1109/CONFLUENCE.2018.8442431

ANewClusteringMethodUsingAnAugmentationToTheSelfOrganizingMaps_Paper-1.pdfAccepted author manuscript, 351 KBLicence: CC BY-ND

https://ieeexplore.ieee.org/abstract/document/8442431

Cite this

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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",

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doi = "10.1109/CONFLUENCE.2018.8442431",

language = "English",

isbn = "978-1-5386-1719-9",

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note = "8th International Conference on Cloud Computing, Data Science & Engineering (Confluence) ; Conference date: 11-01-2018 Through 12-01-2018",

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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

T2 - 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence)

Y2 - 11 January 2018 through 12 January 2018

ER -

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

Abstract

Conference

Keywords

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Cite this