The SpectACl of nonconvex clustering: a spectral approach to density-based clustering

Sibylle Hess, Wouter Duivesteijn, Philipp Honysz, Katharina Morik

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    5 Citations (Scopus)
    53 Downloads (Pure)


    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 ad-vantages 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.
    Original languageEnglish
    Title of host publication33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019
    PublisherAssociation for the Advancement of Artificial Intelligence
    Number of pages8
    ISBN (Electronic)9781577358091
    Publication statusPublished - 2019
    Event33rd AAAI Conference on Artificial Intelligence - Hawaii, Honolulu, United States
    Duration: 27 Jan 20191 Feb 2019
    Conference number: 33


    Conference33rd AAAI Conference on Artificial Intelligence
    Abbreviated titleAAAI-19
    Country/TerritoryUnited States
    Internet address


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