Implementation of rough fuzzy k-means clustering algorithm in Matlab
Jun-Hao Zhang, Ming-Hu Ha, Jing Jyi Wu · 2010
With the assistance of the lower and upper approximation of rough sets, the rough fuzzy k-means clustering algorithm may improve the objective function and further the distribution of membership function for the traditional fuzzy k-means clustering. However, the algorithm only has theoretical ideas rather than concrete realizations. To make it better applied to practice, using Matlab, a mathematical programming tool, to implement rough fuzzy k-means clustering algorithm is discussed. Moreover, steps of implementation are given in detail. The foresaid contributions may provide clustering learners and non-computer professional researchers with a simple, convenient, efficient and feasible implementation method.