Data reduction is conducted by translating a multiple attribute description of an object into k membership values, with respect to k classes which represent the fuzzy behaviour. For further details on the fuzzy k-means algorithm, readers are refer to Sulaiman et al. [33], Dehariya et al. [32], and Jain [34]. In general, the fuzzy k-means classifier uses an iterative procedure that starts with an initial random allocation of the objects to be classified into k clusters. Given the cluster allocation, the centre of each cluster is calculated as the weighted average of the attributes of the objects.