Abstract
The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better.
Highlights
With the rapid development of technologies such as cloud computing and the internet of things [1,2], the number of connected devices is increasing and the data generated during human–computer interaction and system operation is growing exponentially [3,4,5]
Clustering is a method of data mining [9]
In order to explore whether a new distance measure can be obtained by using evidence distance instead of Euclidean distance, an improved k-means algorithm based on evidence distance is proposed in this paper
Summary
With the rapid development of technologies such as cloud computing and the internet of things [1,2], the number of connected devices is increasing and the data generated during human–computer interaction and system operation is growing exponentially [3,4,5]. Tang et al [24] proposed the d-k-means algorithm, which weighs the influence of density and distance on clustering based on traditional algorithms, and weights the data. In order to explore whether a new distance measure can be obtained by using evidence distance instead of Euclidean distance, an improved k-means algorithm based on evidence distance is proposed in this paper. Through validation on the UCI data set and toy data set, and experimental comparison with the traditional kmeans algorithm, and the k-means algorithm based on the aggregation distance parameter and the Gaussian mixture model, the improved k-means algorithm in this paper has better clustering effect and convergence. The third section introduces the algorithmic ideas and motivation of this paper and proposes a k-means algorithm based on evidence distance improvement.
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