  Data visualisation is a key tool in data mining for understanding big
datasets. Many visualisation methods have been proposed, including the
well-regarded state-of-the-art method t-Distributed Stochastic Neighbour
Embedding. However, the most powerful visualisation methods have a significant
limitation: the manner in which they create their visualisation from the
original features of the dataset is completely opaque. Many domains require an
understanding of the data in terms of the original features; there is hence a
need for powerful visualisation methods which use understandable models. In
this work, we propose a genetic programming approach named GPtSNE for evolving
interpretable mappings from a dataset to highquality visualisations. A
multi-objective approach is designed that produces a variety of visualisations
in a single run which give different trade-offs between visual quality and
model complexity. Testing against baseline methods on a variety of datasets
shows the clear potential of GP-tSNE to allow deeper insight into data than
that provided by existing visualisation methods. We further highlight the
benefits of a multi-objective approach through an in-depth analysis of a
candidate front, which shows how multiple models can
