With technology advancement, the data volume and dimensions coming from di˙erent natures has increased considerably. In this scenario, several information visualization techniques, which deal with di˙erent types, structures and data dimensionalities, have arisen and obtained prominence, supporting several knowledge areas, through graphical representations that o˙er a broad and general data view, allowing the their analysis and understanding eÿciently. In this way, Coordinated and Multiple Views have been used to treat the connection between the di˙erent visualization techniques, allowing them to be associated and manipulated more productively, increasing the user’s perception regarding the data set, their relationships and meanings. In this work, new coordination techniques are developed, especially for complex data, applying the similarity concept, which allows to compare data through its content, and diversity, which in the data recovery by similarity, cooperates so that the response set is relevant and e˙ective for the user, adding a level of diversification among them. Through the coordination between visions it is possible to analyze the diversity techniques directly in the graphical representations, checking the datasets as a whole and the neighborhood of their elements, supporting the task of defining an appropriate diversification parameter. We also present the experiments performed in di˙erent scenarios, applying di˙erent datasets, distance metrics and diversity parameters, for the techniques analysis and comparison, according to the response achieved and in the exploratory context of the coordinated views.