# Generalization of k-means Related Algorithms

## TL;DR

This paper explores a generalized approach to center initialization in k-means algorithms, showing that selecting the most distant point from existing centers can replicate the effects of k-means++ initialization.

## Contribution

It introduces a generalized method for center initialization that simplifies and potentially improves upon the k-means++ approach.

## Key findings

- Choosing the most distant point as a new center often yields similar results to k-means++
- The generalized method can simplify the initialization process
- Potential for improved efficiency in clustering algorithms

## Abstract

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.