Inertia-Based Indices to Determine the Number of Clusters in K-Means: An Experimental Evaluation

被引：4
|
作者：
Rykov, Andrei [1 ]
de Amorim, Renato Cordeiro [2 ]
Makarenkov, Vladimir [3 ,4 ]
Mirkin, Boris [1 ,5 ]
机构：
[1] Natl Res Univ Higher Sch Econ, Dept Data Anal & Machine Intelligence, Moscow 101000, Russia
[2] Univ Essex, Comp Sci & Elect Engn Dept, Wivenhoe CO4 3SQ, England
[3] Imagia Cybernet, Montreal, PQ H3C 3P8, Canada
[4] Mila Quebec AI Inst, Montreal, PQ H2S 3H1, Canada
[5] Univ London, Dept Comp Sci & Informat Syst, London WC1E 7HX, England
来源：
IEEE ACCESS | 2024年 / 12卷
基金：
加拿大自然科学与工程研究理事会;
关键词：
Indexes; Clustering algorithms; Euclidean distance; Amplitude modulation; Partitioning algorithms; Computer science; K-means; number of clusters; inertia; elbow method; Calinski-Harabasz index; Hartigan rule; ALGORITHM;
D O I：
10.1109/ACCESS.2024.3350791
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper gives an experimentally supported review and comparison of several indices based on the conventional K-means inertia criterion for determining the number of clusters, K, in datasets, using the popular Silhouette width index as a benchmark. Our experiments involve a novel version of the Elbow index, defined using values of K two or three steps apart. We also discuss alternative ways of computing the inertia and summarizing its values. Even though there are no overall winners in our experiments, some of our results are very conclusive and can be used as a guide for indices determining the number of clusters in K-means.
引用
收藏
页码：11761 / 11773
页数：13
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