# POCS-based Clustering Algorithm

arXiv: https://arxiv.org/abs/2208.08888 · HTML: https://mlsanity.com/paper/2208.08888/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Built on by (most cited first, 1 of 1)

- [Feature Embedding Clustering using POCS-based Clustering Algorithm](https://mlsanity.com/paper/2305.00001/) (2023)

## Abstract (arXiv)

A novel clustering technique based on the projection onto convex set (POCS) method, called POCS-based clustering algorithm, is proposed in this paper. The proposed POCS-based clustering algorithm exploits a parallel projection method of POCS to find appropriate cluster prototypes in the feature space. The algorithm considers each data point as a convex set and projects the cluster prototypes parallelly to the member data points. The projections are convexly combined to minimize the objective function for data clustering purpose. The performance of the proposed POCS-based clustering algorithm is verified through experiments on various synthetic datasets. The experimental results show that the proposed POCS-based clustering algorithm is competitive and efficient in terms of clustering error and execution speed when compared with other conventional clustering methods including Fuzzy C-Means (FCM) and K-means clustering algorithms.