  There has been significant research interest in Procedural Level Generation
via Machine Learning (PLGML), applying ML techniques to automated level
generation. One recent trend is in the direction of learning representations
for level design via embeddings, such as tile embeddings. Tile Embeddings are
continuous vector representations of game levels unifying their visual,
contextual and behavioural information. However, the original tile embedding
struggled to generate levels with skewed tile distributions. For instance,
Super Mario Bros. (SMB) wherein a majority of tiles represent the background.
To remedy this, we present a modified tile embedding representation referred to
as Clustering-based Tile Embedding (CTE). Further, we employ clustering to
discretize the continuous CTE representation and present a novel two-step level
generation to leverage both these representations. We evaluate the performance
of our approach in generating levels for seen and unseen games with skewed tile
distributions and outperform the original tile embeddings.
