Research on improvement of fuzzy K-modes clustering algorithm for categorical data

Cluster analysis is an important method in data mining and has been widely used in various aspects of real life. The Fuzzy K-Modes algorithm is one of the most commonly used algorithms for clustering categorical data due to its simplicity and effectiveness. However, this algorithm has three main problems: sensitivity of initial cluster centers, vulnerability of distance measurement methods to noise, and failure to consider different contributions of attributes during the clustering process. In this paper, we overview some existing improvement algorithms based on the three problems in detail and conduct experimental analysis of these algorithms on real data sets. Furthermore, we summarize the challenges faced in the process of improving the Fuzzy K-Modes algorithm and give possible research directions in future.