Crowdclustering has been recently proposed to engage humans in automated categorization tasks and it shows to be effective especially when digital resources are involved, with complex features to be abstracted for an automated procedure, like images or multimedia resources. In this paper, we propose the HC2 crowdclustering approach for unsupervised classification of digital resources, by allowing the classification categories to dynamically emerge from the crowd. In HC2 , crowd workers actively participate to clustering activities i) by resolving tasks in which they are asked to visually recognize groups of similar resources and ii) by labeling recognized clusters with prominent keywords. To increase flexibility, HC2 can be interactively configured to dynamically set the balance between human engagement and automated procedures in cluster formation, according to the kind and nature of resources to be classified as it will be discussed in the experimental evaluation.