Generalization of k-means Related Algorithms
arXiv:1903.10025v12 citations
Originality Synthesis-oriented
AI Analysis
This provides an incremental improvement to clustering algorithms for data analysis applications.
The paper generalizes the k-means++ algorithm's center initialization process and finds that selecting the most distant sample point from the nearest center as a new center achieves similar performance.
This article briefly introduced Arthur and Vassilvitshii's work on \textbf{k-means++} algorithm and further generalized the center initialization process. It is found that choosing the most distant sample point from the nearest center as new center can mostly have the same effect as the center initialization process in the \textbf{k-means++} algorithm.