Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls

TL;DR

This paper proposes an efficient fuzzy clustering method using local granular balls, improving accuracy and speed in noisy environments by focusing on local data structures and reducing iterative complexity.

Contribution

It introduces a novel fuzzy clustering approach based on local granular balls, enhancing efficiency and robustness compared to traditional global methods.

Findings

Improved clustering accuracy in noisy environments

Reduced computational complexity of fuzzy membership iteration

Enhanced adaptability to different data scenarios

Abstract

In recent years, the problem of fuzzy clustering has been widely concerned. The membership iteration of existing methods is mostly considered globally, which has considerable problems in noisy environments, and iterative calculations for clusters with a large number of different sample sizes are not accurate and efficient. In this paper, starting from the strategy of large-scale priority, the data is fuzzy iterated using granular-balls, and the membership degree of data only considers the two granular-balls where it is located, thus improving the efficiency of iteration. The formed fuzzy granular-balls set can use more processing methods in the face of different data scenarios, which enhances the practicability of fuzzy clustering calculations.