On selecting the Initial Cluster Centers in the K-means Algorithm
2017 IEEE 11th International Conference on Application of Information and Communication Technologies (AICT)(2017)
Abstract
K-means clustering algorithm which is a process of separating n number of points into K clusters according to the predefined value of K is one of the clustering analysis algorithms. This algorithm has many applications in analysis of clustering. There are many factors that affect performance of the K-means clustering algorithm to better cluster. One of these is selecting initial cluster centers. In this study, two methods have been proposed for selecting the initial cluster centers. The proposed methods have been tested on data sets taken from UCI database and compared with the method proposed by Erisoglu etc and K-means algorithm which generates initial centers randomly. The comparison results show that the K-means algorithm which uses the proposed methods converges to better clustering results.
MoreTranslated text
Key words
K-means,Clustering,Data Mining,Initial Cluster Centers
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined