Comparative analysis between k-means and k-medoids for statistical clustering

Norazam Arbin, Nur Suhailayani Suhaimi, Nurul Zafirah Mokhtar, Zalinda Othman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Clustering dynamic data is a challenge inidentifying and forming groups. This unsupervised learningusually leads to undirected knowledge discovery. The clusterdetection algorithm searches for clusters of data which aresimilar to one another by using similarity measures.Determining the suitable algorithm which can bring theoptimized groups cluster could be an issue. Depending on theparameters and attributes of the data, the results yielded fromusing both K-Means and K-Medoids could be varied. Thispaper presents a comparative analysis of both algorithms indifferent data clusters to lay out the strengths and weaknessesof both. Thorough studies were conducted in determining thecorrelation of the data with the algorithms to find therelationship among them.

Original languageEnglish
Title of host publicationProceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages117-121
Number of pages5
ISBN (Electronic)9781467386753
DOIs
Publication statusPublished - 20 Oct 2016
Event3rd International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2015 - Kota Kinabalu, Sabah, Malaysia
Duration: 2 Dec 20154 Dec 2015

Other

Other3rd International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2015
CountryMalaysia
CityKota Kinabalu, Sabah
Period2/12/154/12/15

Fingerprint

K-means
Comparative Analysis
Clustering
Analysis of Algorithms
Data mining
Knowledge Discovery
Similarity Measure
Search Algorithm
Layout
Attribute

Keywords

  • K-Means; K-Medoids; Clustering; Statistic; Data

ASJC Scopus subject areas

  • Artificial Intelligence
  • Modelling and Simulation

Cite this

Arbin, N., Suhaimi, N. S., Mokhtar, N. Z., & Othman, Z. (2016). Comparative analysis between k-means and k-medoids for statistical clustering. In Proceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation (pp. 117-121). [7604562] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIMS.2015.82

Comparative analysis between k-means and k-medoids for statistical clustering. / Arbin, Norazam; Suhaimi, Nur Suhailayani; Mokhtar, Nurul Zafirah; Othman, Zalinda.

Proceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation. Institute of Electrical and Electronics Engineers Inc., 2016. p. 117-121 7604562.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Arbin, N, Suhaimi, NS, Mokhtar, NZ & Othman, Z 2016, Comparative analysis between k-means and k-medoids for statistical clustering. in Proceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation., 7604562, Institute of Electrical and Electronics Engineers Inc., pp. 117-121, 3rd International Conference on Artificial Intelligence, Modelling and Simulation, AIMS 2015, Kota Kinabalu, Sabah, Malaysia, 2/12/15. https://doi.org/10.1109/AIMS.2015.82
Arbin N, Suhaimi NS, Mokhtar NZ, Othman Z. Comparative analysis between k-means and k-medoids for statistical clustering. In Proceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation. Institute of Electrical and Electronics Engineers Inc. 2016. p. 117-121. 7604562 https://doi.org/10.1109/AIMS.2015.82
Arbin, Norazam ; Suhaimi, Nur Suhailayani ; Mokhtar, Nurul Zafirah ; Othman, Zalinda. / Comparative analysis between k-means and k-medoids for statistical clustering. Proceedings - AIMS 2015, 3rd International Conference on Artificial Intelligence, Modelling and Simulation. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 117-121
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