SSFCM-FWCW: Semi-Supervised Fuzzy C-Means method based on Feature-Weight and Cluster-Weight learning
Amin Golzari Oskouei, Negin Samadi, Jafar Tanha, Asgarali Bouyer
Abstract
SSFCM-FWCW (Feature-Weight and Cluster-Weight based Semi-Supervised Fuzzy C -Means) is a soft clustering method. It incorporates supplementary label information to enhance the clustering quality. An adaptive local feature weighting technique is utilized to weight features based on their significance within specific clusters. Additionally, an adaptive weighting technique is applied to diminish the sensitivity to the initial center selection, effectively distinguishing between the effects of various clusters. The conjunction of label information and adaptive weighting results in an optimal fuzzy c -means clustering with an insight into the importance of individual features and clusters. An open-source Matlab implementation of SSFCM-FWCW is available. • We provide a MATLAB implementation of Feature-Weight and Cluster-Weight-based Fuzzy c-Means for Semi-Supervised clustering. • The code is helpful to machine learning researchers as well as practitioners. • The code is an efficient, extensible, scalable, readable, portable and easy to use. • Allows easy integration of new methods in a modular fashion.