# Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity

arXiv: https://arxiv.org/abs/1908.06699 · HTML: https://mlsanity.com/paper/1908.06699/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

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

- [Accelerated Fuzzy C-Means Clustering Based on New Affinity Filtering and Membership Scaling](https://mlsanity.com/paper/2302.07060/) (2023)

## Abstract (arXiv)

Clustering is an effective technique in data mining to group a set of objects in terms of some attributes. Among various clustering approaches, the family of K-Means algorithms gains popularity due to simplicity and efficiency. However, most of existing K-Means based clustering algorithms cannot deal with outliers well and are difficult to efficiently solve the problem embedded the L0-norm constraint. To address the above issues and improve the performance of clustering significantly, we propose a novel clustering algorithm, named REFCMFS, which develops a L2,1-norm robust loss as the data-driven item and imposes a L0-norm constraint on the membership matrix to make the model more robust and sparse flexibly. In particular, REFCMFS designs a new way to simplify and solve the L0-norm constraint without any approximate transformation by absorbing |·|0 into the objective function through a ranking function. These improvements not only make REFCMFS efficiently obtain more promising performance but also provide a new tractable and skillful optimization method to solve the problem embedded the L0-norm constraint. Theoretical analyses and extensive experiments on several public datasets demonstrate the effectiveness and rationality of our proposed REFCMFS method.