Open AccessImproved K-means Algorithm Based on Threshold Value Radius
Author(s) -
Jiaqi Song,
Fēi Li,
Ruxiang Li
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/428/1/012001
Subject(s) - cluster analysis , radius , algorithm , threshold limit value , euclidean distance , value (mathematics) , center (category theory) , computer science , k medoids , mathematics , canopy clustering algorithm , correlation clustering , artificial intelligence , machine learning , medicine , chemistry , computer security , crystallography , environmental health
K-means algorithm is a classical clustering algorithm, which needs to specify K value artificially and chooses the initial clustering center randomly. However, it is easy to fall into local optimal solution. In order to overcome the shortcomings of K-means algorithm, an improved algorithm based on threshold value radius is proposed in this paper. The initial clustering center and threshold radius are determined automatically by the average value of Euclidean distance between data. The experimental results show that the method proposed can effectively and accurately overcome the shortcomings of the traditional K-means algorithm.