K-means clustering algorithm based on Optimization of initial clustering center

Konferenz: CIBDA 2022 - 3rd International Conference on Computer Information and Big Data Applications
25.03.2022 - 27.03.2022 in Wuhan, China

Tagungsband: CIBDA 2022

Seiten: 4Sprache: EnglischTyp: PDF

Autoren:
Huang, Suyu (Wuhan Donghu University, Wuhan, Hubei, China)

Inhalt:
K-means clustering algorithm based on initial cluster center optimization. Aiming at the fact that the running result of traditional K-means clustering algorithm depends on the initial cluster number and cluster center, this paper proposes a k-means algorithm based on initial cluster center optimization. According to the distribution characteristics of the data set, the algorithm selects the data far away as the initial clustering center, and determines the clustering number k value through the clustering compactness and quantifying the distance between samples. Experiments show that the improved clustering algorithm proposed in this paper obtains good clustering effect and high clustering accuracy.