to real world limitations such as noise, shadowing, and variations in cameras, traditional hard clustering is often unable to reliably perform image processing tasks as stated above. Fuzzy clustering has been proposed as a more applicable algorithm in the performance to these tasks. Given is gray scale image that has undergone fuzzy clustering in Matlab. The original image is seen next to a clustered image. Colors are used to give a visual representation of the three distinct clusters used to identify the membership of each pixel. Below, a chart is given that defines the fuzzy membership coefficients of their corresponding intensity values. Depending on the application for which the fuzzy clustering coefficients are to be used, different pre-processing techniques can be applied to RGB images. RGB to HCL conversion is common practice. See also FLAME Clustering Cluster Analysis Expectation-maximization algorithm (a similar, but more statistically formalized method) References Cluster analysis algorithms