Optimisation of K-means algorithm based on sample density canopy. (19th November 2021) - Shen, Guo-xin; Jiang, Zhong-yun
Since the random selection of the initial centroid and the artificial definition of the number of clusters affect the experimental results of K-means, therefore, this article uses sample density and canopy to optimise the K-means algorithm. This algorithm first calculates the sample density of each data, and selects the data point with the smallest density as the first cluster centroid; then combines the canopy algorithm to cluster the original sample data to obtain the number of clusters and each cluster centre. As initial parameter of the K-means finally combines the K-means algorithm to assemble the original samples, UCI dataset and self-built dataset were used to compare simulation experiments. The results show that the algorithm can make clustering results more accurate, run faster, and improve the stability of the algorithm.