Guided Filter-Based Fuzzy Clustering for General Data Analysis.

In recent years, benefiting from the abilities in implementation of the smooth operation on noises and preservation of the original gradient information on the texture edges, the guided filter (GF)-based fuzzy clustering has achieved inspiring performance in the task of image segmentation. However, different to image pixels, the general data samples do not have the spatial adjacency relations. Current GF-based methods suffer some limitations in general data clustering. To solve this issue, a series of improved GF-based fuzzy clustering algorithms are proposed in this paper for general data analysis. In the most basic algorithm, a new data filtering window is first defined according to the neighbor relationships between data samples, and a pruning mechanism is designed to ensure the symmetry of neighbor samples. Then, the GF-based Fuzzy C-Means algorithm is put forward for the general data clustering. In addition, a weighted version is presented to process high-dimensional data, in which each dimension is assigned a weight, and the entropy regularization method is applied to optimize the weight assignment and highlight the important dimensions on both filtering and clustering. Furthermore, the kernelization of the above methods are realized for the non-linear data. Experimental results on synthetic and real-world datasets demonstrate better performance of the proposed methods in comparison with some existing FCM-type clustering approaches. [ABSTRACT FROM AUTHOR]