# clustering an african hairstyle dataset using pca and k-means

Source: [clustering an african hairstyle dataset using pca and k-means](<https://www.sota2.com/research/paper/68fa6fbd1e209131cea4727f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:26:24.585Z

Authors: Teffo Phomolo Nicrocia, Owolawi Pius Adewale, Pholo Moanda Diana

Published: 2023-05-25T14:13:29.000Z

## Abstract

The adoption of digital transformation was not expressed in building an African face shape classifier. In this paper, an approach is presented that uses k-means to classify African women images. African women rely on beauty standards recommendations, personal preference, or the newest trends in hairstyles to decide on the appropriate hairstyle for them. In this paper, an approach is presented that uses K-means clustering to classify African women's images. In order to identify potential facial clusters, Haarcascade is used for feature-based training, and K-means clustering is applied for image classification.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2306.06061>)

[PDF](<https://arxiv.org/pdf/2306.06061.pdf>)