The second group uses patterns with 'mismatch representation' which define a signal to be a consensus pattern and allow up to a certain number of mismatches to occur in each instance of the pattern. The goal of these algorithms is to recover the consensus pattern with the most significant number of instances, given a certain background model. These methods view the representation of the signals as discrete and the main advantage of these algorithms is that they can guarantee that the highest scoring pattern will be the global optimum for any scoring function. The disadvantage, however, is that consensus patterns are not as expressive of the DNA signal as profile representations. Recent approaches within this framework include Projection methods [1,11], string based methods [2], Pattern-Branching [12], MULTIPROFILER [13] and other branch and bound approaches [7,14].