Abstract
We have proposed L2 or L1-norm based tolerant fuzzy c-means clustering (TFCM) from the viewpoint of handling data more flexibly.
This paper presents a new type of tolerant fuzzy c-means clustering with L1-regularization.
The L1-regularization is well-known as the most successful technique to induce sparseness.
The proposed algorithm induce the sparseness for tolerance vector. First, tolerant fuzzy c-means clustering is introduced.
Second, the optimization problems with L1-regularization are solved. Third, a new clustering algorithm is constructed based on the explicit
optimal solutions.
Finally, the effectiveness of the proposed algorithm is verified through numerical examples.
