Clustering of Fuzzy K-Means With Discriminative Embedding: A Review
DOI : 10.46335/IJIES.2024.9.4.20
Authors : Payal Rajput, Prof. Nilesh S. Vani
Abstract – A popular clustering technique called fuzzy K-means (FKM) divides each data point into one or more groups according to how far it is from each cluster's centroid. Nonetheless, methods for mapping the data into a lower-dimensional space where the clustering can be carried out more successfully have been developed, such as discriminative embedding approaches. The current state of FKM clustering with discriminative embedding is reviewed in this paper, along with the primary methods and their uses. The difficulties and potential future directions of this field of study are also discussed.
