Fuzzy clustering | MATLAB realizes data analysis based on FC fuzzy clustering
Table of contents
Effectiveness analysis
basic introduction
Fuzzy clustering is a clustering method. Different from the traditional hard clustering (Hard Clustering) method, Fuzzy Clustering (Fuzzy Clustering) does not rigidly assign each data point to a cluster center, but considers each In the case that data points may belong to multiple cluster centers, the probability distribution of each data point belonging to each cluster center is given, that is, the degree of membership of each data point on each cluster center. This approach can effectively handle the case where data points may belong to multiple clusters, as well as the presence of noisy data.
In fuzzy clustering, the result of clustering is a probability distribution matrix, which represents the probability that each data point belongs to each cluster center. This matrix can be used to analyze the cluster distribution of data points, and it can also be used to make cluster predictions for new data points. Common fuzzy clustering methods include Fuzzy C-Means (FCM) and Gustafson-Kessel