  When it comes to clustering nonconvex shapes, two paradigms are used to find
the most suitable clustering: minimum cut and maximum density. The most popular
algorithms incorporating these paradigms are Spectral Clustering and DBSCAN.
Both paradigms have their pros and cons. While minimum cut clusterings are
sensitive to noise, density-based clusterings have trouble handling clusters
with varying densities. In this paper, we propose \textsc{SpectACl}: a method
combining the advantages of both approaches, while solving the two mentioned
drawbacks. Our method is easy to implement, such as spectral clustering, and
theoretically founded to optimize a proposed density criterion of clusterings.
Through experiments on synthetic and real-world data, we demonstrate that our
approach provides robust and reliable clusterings.

Machine Learning;Machine Learning