The rapid evolution of computing resources has enabled large datasets to be stored and retrieved.However, exploring, understanding and extracting useful information is still a challenge.Among the computational tools to address this problem, information visualization techniques enable the data analysis employing the human visual ability by making a graphic representation of the data set, and data mining provides automatic processes for the discovery and interpretation of patterns.Despite the recent popularity of information visualization methods, a recurring problem is the low visual scalability when analyzing large data sets resulting in context loss and visual disorder.To represent large datasets reducing the loss of relevant information, the process of aggregation is being used.Aggregation decreases the amount of data to be represented, preserving the distribution and trends of the original dataset.Regarding data mining, information visualization has become an essential tool in the interpretation of computational models and generated results, especially of unsupervised techniques, such as clustering.This occurs because, in these techniques, the only way the user interacts with the mining process is through parameterization, limiting the insertion of domain knowledge in the process.In this thesis, we propose and develop the new visual metaphor based on the TableLens that employs approaches based on the concept of aggregation to create more scalable representations of tabular data.As application, we use the developed metaphor in the analysis of the results of clustering techniques.The resulting framework does not only support large database analysis but also provides insights into how data attributes contribute to clustering regarding cohesion and separation of the composed groups