The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering

  • Sibylle Hess Technische Universität Dortmund
  • Wouter Duivesteijn Technische Universität Eindhoven
  • Philipp Honysz Technische Universität Dortmund
  • Katharina Morik Technische Universität Dortmund

Abstract

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 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.

Published
2019-07-17
Section
AAAI Technical Track: Machine Learning