Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity
Abstract
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 -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 -norm robust loss as the data-driven item and imposes a -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 -norm constraint without any approximate transformation by absorbing 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 -norm constraint. Theoretical analyses and extensive experiments on several public datasets demonstrate the effectiveness and rationality of our proposed REFCMFS method.
Cite
@article{arxiv.1908.06699,
title = {Robust and Efficient Fuzzy C-Means Clustering Constrained on Flexible Sparsity},
author = {Jinglin Xu and Junwei Han and Mingliang Xu and Feiping Nie and Xuelong Li},
journal= {arXiv preprint arXiv:1908.06699},
year = {2019}
}