dc.contributor.author |
Onumanyi, Adeiza J
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|
dc.contributor.author |
Molokomme, Daisy N
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|
dc.contributor.author |
Isaac, Sherrin J
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|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
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|
dc.date.accessioned |
2022-08-22T08:19:02Z |
|
dc.date.available |
2022-08-22T08:19:02Z |
|
dc.date.issued |
2022 |
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dc.identifier.citation |
Onumanyi, A.J., Molokomme, D.N., Isaac, S.J. & Abu-Mahfouz, A.M. 2022. AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset. <i>Applied Sciences-Basel, 12(15).</i> http://hdl.handle.net/10204/12479 |
en_ZA |
dc.identifier.issn |
2076-3417 |
|
dc.identifier.uri |
https://doi.org/10.3390/app12157515
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|
dc.identifier.uri |
http://hdl.handle.net/10204/12479
|
|
dc.description.abstract |
The elbow technique is a well-known method for estimating the number of clusters required as a starting parameter in the K-means algorithm and certain other unsupervised machine-learning algorithms. However, due to the graphical output nature of the method, human assessment is necessary to determine the location of the elbow and, consequently, the number of data clusters. This article presents a simple method for estimating the elbow point, thus, enabling the K-means algorithm to be readily automated. First, the elbow-based graph is normalized using the graph’s minimum and maximum values along the ordinate and abscissa coordinates. Then, the distance between each point on the graph to the minimum (i.e., the origin) and maximum reference points, and the “heel” of the graph are calculated. The estimated elbow location is, thus, the point that maximizes the ratio of these distances, which corresponds to an approximate number of clusters in the dataset. We demonstrate that the strategy is effective, stable, and adaptable over different types of datasets characterized by small and large clusters, different cluster shapes, high dimensionality, and unbalanced distributions. We provide the clustering community with a description of the method and present comparative results against other well-known methods in the prior state of the art. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.mdpi.com/2076-3417/12/15/7515/htm |
en_US |
dc.source |
Applied Sciences-Basel, 12(15) |
en_US |
dc.subject |
Clustering |
en_US |
dc.subject |
Elbow method |
en_US |
dc.subject |
K-means algorithm |
en_US |
dc.title |
AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
17 |
en_US |
dc.description.note |
Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
Advanced Internet of Things |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Onumanyi, A. J., Molokomme, D. N., Isaac, S. J., & Abu-Mahfouz, A. M. (2022). AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset. <i>Applied Sciences-Basel, 12(15)</i>, http://hdl.handle.net/10204/12479 |
en_ZA |
dc.identifier.chicagocitation |
Onumanyi, Adeiza J, Daisy N Molokomme, Sherrin J Isaac, and Adnan MI Abu-Mahfouz "AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset." <i>Applied Sciences-Basel, 12(15)</i> (2022) http://hdl.handle.net/10204/12479 |
en_ZA |
dc.identifier.vancouvercitation |
Onumanyi AJ, Molokomme DN, Isaac SJ, Abu-Mahfouz AM. AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset. Applied Sciences-Basel, 12(15). 2022; http://hdl.handle.net/10204/12479. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Onumanyi, Adeiza J
AU - Molokomme, Daisy N
AU - Isaac, Sherrin J
AU - Abu-Mahfouz, Adnan MI
AB - The elbow technique is a well-known method for estimating the number of clusters required as a starting parameter in the K-means algorithm and certain other unsupervised machine-learning algorithms. However, due to the graphical output nature of the method, human assessment is necessary to determine the location of the elbow and, consequently, the number of data clusters. This article presents a simple method for estimating the elbow point, thus, enabling the K-means algorithm to be readily automated. First, the elbow-based graph is normalized using the graph’s minimum and maximum values along the ordinate and abscissa coordinates. Then, the distance between each point on the graph to the minimum (i.e., the origin) and maximum reference points, and the “heel” of the graph are calculated. The estimated elbow location is, thus, the point that maximizes the ratio of these distances, which corresponds to an approximate number of clusters in the dataset. We demonstrate that the strategy is effective, stable, and adaptable over different types of datasets characterized by small and large clusters, different cluster shapes, high dimensionality, and unbalanced distributions. We provide the clustering community with a description of the method and present comparative results against other well-known methods in the prior state of the art.
DA - 2022
DB - ResearchSpace
DP - CSIR
J1 - Applied Sciences-Basel, 12(15)
KW - Clustering
KW - Elbow method
KW - K-means algorithm
LK - https://researchspace.csir.co.za
PY - 2022
SM - 2076-3417
T1 - AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset
TI - AutoElbow: An automatic elbow detection method for estimating the number of clusters in a dataset
UR - http://hdl.handle.net/10204/12479
ER -
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en_ZA |
dc.identifier.worklist |
25952 |
en_US |