An initialization method for the k-means using the concept of useful nearest centers

TL;DR

This paper introduces a new initialization method for k-means clustering that uses the concept of useful nearest centers to improve the selection of initial seeds, aiming to enhance clustering performance.

Contribution

It proposes a novel initialization technique based on useful nearest centers, addressing the sensitivity of k-means to initial seed selection.

Findings

Improved clustering stability and accuracy.

Reduced sensitivity to initial seed choice.

Enhanced convergence speed.

Abstract

The aim of the k-means is to minimize squared sum of Euclidean distance from the mean (SSEDM) of each cluster. The k-means can effectively optimize this function, but it is too sensitive for initial centers (seeds). This paper proposed a method for initialization of the k-means using the concept of useful nearest center for each data point.