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A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm

EURASIP journal on wireless communications and networking, 2021-02, Vol.2021 (1), p.1-16, Article 31 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1687-1499 ;ISSN: 1687-1472 ;EISSN: 1687-1499 ;DOI: 10.1186/s13638-021-01910-w

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  • Title:
    A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm
  • Author: Shi, Congming ; Wei, Bingtao ; Wei, Shoulin ; Wang, Wen ; Liu, Hai ; Liu, Jialei
  • Subjects: Algorithms ; Clustering ; Communications Engineering ; Cosine law ; Data analysis ; Datasets ; Edge ; Elbow method ; Engineering ; Fog ; Human-centered Computing in Cloud ; Information Systems Applications (incl.Internet) ; Machine learning ; Mathematical analysis ; Networks ; Signal,Image and Speech Processing ; Silhouette coefficient
  • Is Part Of: EURASIP journal on wireless communications and networking, 2021-02, Vol.2021 (1), p.1-16, Article 31
  • Description: Clustering, a traditional machine learning method, plays a significant role in data analysis. Most clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although the Elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on the manual identification of the elbow points on the visualization curve. Thus, experienced analysts cannot clearly identify the elbow point from the plotted curve when the plotted curve is fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to yield a statistical metric that estimates an optimal cluster number when clustering on a dataset. First, the average degree of distortion obtained by the Elbow method is normalized to the range of 0 to 10. Second, the normalized results are used to calculate the cosine of intersection angles between elbow points. Third, this calculated cosine of intersection angles and the arccosine theorem are used to compute the intersection angles between elbow points. Finally, the index of the above-computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a well-known public dataset (Iris Dataset) demonstrated that the estimated optimal cluster number obtained by our newly proposed method is better than the widely used Silhouette method.
  • Publisher: Cham: Springer International Publishing
  • Language: English
  • Identifier: ISSN: 1687-1499
    ISSN: 1687-1472
    EISSN: 1687-1499
    DOI: 10.1186/s13638-021-01910-w
  • Source: SpringerOpen
    DOAJ Directory of Open Access Journals
    AUTh Library subscriptions: ProQuest Central
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