The massive generation of unlabeled data of current industrial applications has attracted the interest of data mining practitioners. Thus, retrieving novel and useful information from these volumes of data while decreasing the costs of manipulating such amounts of information is a major issue. Multiobjective clustering algorithms are able to recognize patterns considering several objective function which is crucial in real-world situations. However, they dearth from a retrieval system for obtaining the most suitable solution, and due to the fact that the size of Pareto set can be unpractical for human experts, autonomous retrieval methods are fostered. This paper presents an automatic retrieval system for handling Pareto-based multiobjective clustering problems based on the shape of the Pareto set and the quality of the clusters. The proposed method is integrated in CAOS, a scalable and flexible framework, to test its performance. Our approach is compared to classic retrieval methods that only consider individual strategies by using a wide set of artificial and real-world datasets. This filtering approach is evaluated under large data volumes demonstrating its competence in clustering problems. Experiments support that the proposal overcomes the accuracy and significantly reduces the computational time of the solution retrieval achieved by the individual strategies.