Combining Pareto-Optimal Clusters Using Supervised Learning

The multiobjective fuzzy genetic clustering technique described in the previous chapter simultaneously optimizes the Xie-Beni (XB) index [442] and the fuzzy Cmeans (FCM) [62] measure (Jm). In multiobjective optimization (MOO), a search is performed over a number of, often conflicting, objective functions. Instead of yielding a single best solution, MOO yields the final solution set containing a number of non-dominated Pareto-optimal solutions. A characteristic of the multiobjective fuzzy clustering approach is that it often produces a large number of Pareto-optimal solutions, from which selecting a particular solution is difficult.