Unrestricted access to both historical and archaeological sites is highly desirable from both a research and a cultural perspective. However, due to security and preservation considerations, access is becoming more and more restricted and subject to various conditions. With the recent developments in 3D scanner technologies and photogrammetric techniques, it is now possible to acquire and create accurate models of such sites. Through the process of virtualisation, numerous virtual collections are created that need to be visualised, searched and eventually characterized. This paper presents a mobile virtual environment designed for the visualization of photorealistic high-resolution virtualised scenes and artefacts. The mobile virtual environment also includes a component for retrieving artefacts from virtual collections. This stereo virtual environment is portable and can be easily and rapidly deployed at any suitable location, for instance an archaeological site. The architecture and the implementation of the mobile virtual environment are described. This environment is characterized by a massively asynchronous architecture that optimises the rendering performances by distributing the calculations over various graphical processing units. A request broker insures the synchronization among the various components of the system. The performance of the system is illustrated through multiple examples of the visualisation of virtualized cultural heritage sites. In addition, it is shown how it is possible to describe the geometry of the artefacts by representing them with compact support feature vectors. A recurrent data mining system, based on these vectors, is presented. This system allows the characterization and exploration of the collection, through cluster analysis. The system employs the "query by example" paradigm and the knowledge of the expert in a recurrent approach, in order to identify clusters of artefacts. The virtual environment is subsequently utilised in order to perform visual data mining on the clusters, as identified during data mining, and to characterize and further explore the clusters by defining archetypes.