Looking back on the past decade of research on clustering algorithms, we witness two major and apparent trends: 1) The already vast amount of existing clustering algorithms, is continuously broadened and 2) clustering algorithms in general, are becoming more and more adapted to specific application domains with very particular assumptions. As a result, algorithms have grown complicated and/or very scenariodependent, which made clustering a hardly accessible domain for non-expert users. This is an especially critical development, since, due to increasing data gathering, the need for analysis techniques like clustering emerges in many application domains. In this paper, we oppose the current focus on specialization, by proposing our vision of a usable, guided and universally applicable clustering process. In detail, we are going to describe our already conducted work and present our future research directions.