# Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)

## TL;DR

This paper addresses the challenge of selecting initial centroids in k-means clustering, proposing a novel distance-based method to improve clustering accuracy especially as the number of clusters grows.

## Contribution

It introduces a new distance part approach for initial centroid selection in k-means, enhancing clustering performance over traditional methods.

## Key findings

- Improved clustering accuracy with the proposed method
- More stable initial centroid selection in large cluster scenarios
- Reduced sensitivity to initial centroid choice

## Abstract

The initial centroid is a fairly challenging problem in the k-means method because it can affect the clustering results. In addition, choosing the starting centroid of the cluster is not always appropriate, especially, when the number of groups increases.

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Source: https://tomesphere.com/paper/1903.07977