Optimisation of K-means algorithm based on sample density canopy. (19th November 2021)

• Record Type: Journal Article
• Authors: Shen, Guo-xin; Jiang, Zhong-yun
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 38:Number 1/3(2021)
• Page Start: 62
• Page End: 69
• Publication Date: 2021-11-19
• Subjects: clustering -- K-means algorithm -- density -- neighbourhood -- initial centroid; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

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.