Research on Efficient Fuzzy Clustering Method Based on Local Fuzzy Granular balls
This work addresses efficiency and accuracy issues in fuzzy clustering for noisy and imbalanced data, representing an incremental improvement over existing methods.
The paper tackles the problem of fuzzy clustering's inefficiency and inaccuracy in noisy environments and with varying sample sizes by introducing a local fuzzy granular-balls method that iterates membership based on two granular-balls, improving iteration efficiency and enhancing practicality for different data scenarios.
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.