The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based
Clustering
Sibylle Hess, Wouter Duivesteijn, Philipp-Jan Honysz, Katharina Morik
When it comes to the clustering of nonconvex shapes, generally 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 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.
By means of experiments on synthetic and real-world data,
we demonstrate that our approach provides robust and reliable
clusterings.
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