Algorithms map input spaces to output spaces where inputs are possibly affected by fluctuations. Beside run time and memory consumption, an algorithm might be characterized by its sensitivity to the signal in the input and its robustness to input fluctuations. The achievable precision of an algorithm, i.e., the attainable resolution in its output space, is determined by capability to extract predictive information in the input relative to its output. I will present an information theoretic framework for algorithm analysis where an algorithm is characterized as computational evolution of a (possibly contracting) posterior distribution over the output space. The tradeoff between precision and stability is controlled by an input sensitive generalization capacity (GC). GC measures how much the posteriors on two different problem instances agree despite the noise in the input. Thereby, GC objectively ranks different algorithms for the same data processing task based on the bit rate of their respective capacities. Information theoretic algorithm selection is demonstrated for minimum spanning tree algorithms and for greedy MaxCut algorithms. The method can rank centroid based and spectral clustering methods, e.g. k-means, pairwise clustering, normalized cut, adaptive ratio cut and dominant set clustering.