Performance Analysis of K Means Clustering Algorithms for mMTC Systems

Haesik Kim

doi:10.1109/ICTC49870.2020.9289287

Performance Analysis of K Means Clustering Algorithms for mMTC Systems

^*Corresponding author for this work

BA6402 Future communication networks

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

9 Citations (Scopus)

Abstract

In 5G, we generate a huge amount of data everyday due to high capacity network systems. Many research groups paid attention to machine learning algorithms in order to deal with big data and massive connection. The mMTC systems are one of key 5G applications. It requires massive connection. Clustering is one of key research challenges to design mMTC systems. K-means clustering algorithm is one of the simplest unsupervised machine learning algorithms. The purpose of this algorithm is to find a cluster in data by iteratively minimizing the measure between the cluster centre of the group and the given observation. In this paper, K means clustering algorithms are applied for mMTC clustering problem. New metrics for clustering mMTC devices are proposed. Their performances are investigated and analyzed under the given simulation configuration.

Original language	English
Title of host publication	2020 International Conference on Information and Communication Technology Convergence (ICTC)
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	30-35
ISBN (Electronic)	978-1-7281-6758-9
ISBN (Print)	978-1-7281-9901-6
DOIs	https://doi.org/10.1109/ICTC49870.2020.9289287
Publication status	Published - 21 Dec 2020
MoE publication type	A4 Article in a conference publication
Event	11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of Duration: 21 Oct 2020 → 23 Oct 2020

Publication series

Series	International Conference on ICT Convergence
Volume	2020-October
ISSN	2162-1233

Conference

Conference	11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/Territory	Korea, Republic of
City	Jeju Island
Period	21/10/20 → 23/10/20

Funding

This work was supported by the European Commission in the framework of the H2020-ICT-19-2019 project 5G-HEART (Grant agreement no. 857034).

Keywords

Clustering
etc
IoT
K means clustering
Machine learning
mMTC
Sensor networks
Unsupervised learning
Wireless Communication

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10.1109/ICTC49870.2020.9289287

Cite this

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title = "Performance Analysis of K Means Clustering Algorithms for mMTC Systems",

abstract = "In 5G, we generate a huge amount of data everyday due to high capacity network systems. Many research groups paid attention to machine learning algorithms in order to deal with big data and massive connection. The mMTC systems are one of key 5G applications. It requires massive connection. Clustering is one of key research challenges to design mMTC systems. K-means clustering algorithm is one of the simplest unsupervised machine learning algorithms. The purpose of this algorithm is to find a cluster in data by iteratively minimizing the measure between the cluster centre of the group and the given observation. In this paper, K means clustering algorithms are applied for mMTC clustering problem. New metrics for clustering mMTC devices are proposed. Their performances are investigated and analyzed under the given simulation configuration.",

keywords = "Clustering, etc, IoT, K means clustering, Machine learning, mMTC, Sensor networks, Unsupervised learning, Wireless Communication",

author = "Haesik Kim",

note = "Funding Information: This work was supported by the European Commission in the framework of the H2020-ICT-19-2019 project 5G-HEART (Grant agreement no. 857034). Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 ; Conference date: 21-10-2020 Through 23-10-2020",

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doi = "10.1109/ICTC49870.2020.9289287",

language = "English",

isbn = "978-1-7281-9901-6",

series = "International Conference on ICT Convergence",

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Kim, H 2020, Performance Analysis of K Means Clustering Algorithms for mMTC Systems. in 2020 International Conference on Information and Communication Technology Convergence (ICTC)., 9289287, IEEE Institute of Electrical and Electronic Engineers, International Conference on ICT Convergence, vol. 2020-October, pp. 30-35, 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, Jeju Island, Korea, Republic of, 21/10/20. https://doi.org/10.1109/ICTC49870.2020.9289287

Performance Analysis of K Means Clustering Algorithms for mMTC Systems. / Kim, Haesik.
2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE Institute of Electrical and Electronic Engineers, 2020. p. 30-35 9289287 (International Conference on ICT Convergence, Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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N1 - Funding Information: This work was supported by the European Commission in the framework of the H2020-ICT-19-2019 project 5G-HEART (Grant agreement no. 857034). Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/12/21

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N2 - In 5G, we generate a huge amount of data everyday due to high capacity network systems. Many research groups paid attention to machine learning algorithms in order to deal with big data and massive connection. The mMTC systems are one of key 5G applications. It requires massive connection. Clustering is one of key research challenges to design mMTC systems. K-means clustering algorithm is one of the simplest unsupervised machine learning algorithms. The purpose of this algorithm is to find a cluster in data by iteratively minimizing the measure between the cluster centre of the group and the given observation. In this paper, K means clustering algorithms are applied for mMTC clustering problem. New metrics for clustering mMTC devices are proposed. Their performances are investigated and analyzed under the given simulation configuration.

AB - In 5G, we generate a huge amount of data everyday due to high capacity network systems. Many research groups paid attention to machine learning algorithms in order to deal with big data and massive connection. The mMTC systems are one of key 5G applications. It requires massive connection. Clustering is one of key research challenges to design mMTC systems. K-means clustering algorithm is one of the simplest unsupervised machine learning algorithms. The purpose of this algorithm is to find a cluster in data by iteratively minimizing the measure between the cluster centre of the group and the given observation. In this paper, K means clustering algorithms are applied for mMTC clustering problem. New metrics for clustering mMTC devices are proposed. Their performances are investigated and analyzed under the given simulation configuration.

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Performance Analysis of K Means Clustering Algorithms for mMTC Systems

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