The concept of involving users in the loop of analytic workflows refers to the ability to replace heuristics with user input inmachine learning and data mining tasks. For supervised tasks, user engagement generally occurs via the manipulationof training data. But for unsupervised tasks, user involvement is limited to changes in the algorithm parametrization orthe input data representation, also known as features. Typically, different types of features can be extracted from rawdata, and the careful selection of the extraction strategy allows users to have more control over unsupervised tasks.Nevertheless, since there is no perfect feature extractor, the combination of multiple sets of features has been exploredthrough a process called feature fusion. Feature fusion can be readily performed when the machine learning or datamining algorithms have a cost function, such as accuracy for classification tasks. However, when such a function doesnot exist, user support needs to be provided otherwise the process is impractical. In this work, we present a novelfeature fusion approach that employs data samples and visualization to allow users to not only effortlessly control thecombination of different feature sets but also to understand the attained results.