Selection of K in K-means clustering

被引：350
作者：
Pham, DT [1 ]
Dimov, SS [1 ]
Nguyen, CD [1 ]
机构：
[1] Cardiff Univ, Mfg Engn Ctr, Cardiff CF24 OYF, Wales
来源：
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE | 2005年 / 219卷 / 01期
关键词：
clustering; K-means algorithm; cluster number selection;
D O I：
10.1243/095440605X8298
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
The K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed. The paper concludes with an analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different data sets.
引用
收藏
页码：103 / 119
页数：17
相关论文
共 50 条
  • [21] Family of K-Means Clustering for Robust Mean-Variance Portfolio Selection: A Comparison of K-Medoids, K-Means, and Fuzzy C-Means
    Gubu, La
    Cahyono, Edi
    Budiman, Herdi
    Djafar, Muh. Kabil
    [J]. INDUSTRIAL ENGINEERING AND MANAGEMENT SYSTEMS, 2024, 23 (03): : 342 - 356
  • [22] Unsupervised K-Means Clustering Algorithm
    Sinaga, Kristina P.
    Yang, Miin-Shen
    [J]. IEEE ACCESS, 2020, 8 : 80716 - 80727
  • [23] Dynamic Incremental K-means Clustering
    Aaron, Bryant
    Tamir, Dan E.
    Rishe, Naphtali D.
    Kandel, Abraham
    [J]. 2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), VOL 1, 2014, : 308 - 313
  • [24] APPLICATION OF METAHEURISTICS TO K-MEANS CLUSTERING
    Lisin, A. V.
    Faizullin, R. T.
    [J]. COMPUTER OPTICS, 2015, 39 (03) : 406 - 412
  • [25] The LINEX Weighted k-Means Clustering
    Ahmadzadehgoli, Narges
    Mohammadpour, Adel
    Behzadi, Mohammad Hassan
    [J]. JOURNAL OF STATISTICAL THEORY AND APPLICATIONS, 2019, 18 (02): : 147 - 154
  • [26] Locality Sensitive K-means Clustering
    Liu, Chlen-Liang
    Hsai, Wen-Hoar
    Chang, Tao-Hsing
    [J]. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 2018, 34 (01) : 289 - 305
  • [27] Modified k-Means Clustering Algorithm
    Patel, Vaishali R.
    Mehta, Rupa G.
    [J]. COMPUTATIONAL INTELLIGENCE AND INFORMATION TECHNOLOGY, 2011, 250 : 307 - +
  • [28] The MinMax k-Means clustering algorithm
    Tzortzis, Grigorios
    Likas, Aristidis
    [J]. PATTERN RECOGNITION, 2014, 47 (07) : 2505 - 2516
  • [29] Random Projection for k-means Clustering
    Sieranoja, Sami
    Franti, Pasi
    [J]. ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2018, PT I, 2018, 10841 : 680 - 689
  • [30] A notion of stability for k-means clustering
    Le Gouic, T.
    Paris, Q.
    [J]. ELECTRONIC JOURNAL OF STATISTICS, 2018, 12 (02): : 4239 - 4263
12345