Clustering based on density estimation with sparse grids

Benjamin Peherstorfer, Dirk Pflüger, Hans Joachim Bungartz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


We present a density-based clustering method. The clusters are determined by splitting a similarity graph of the data into connected components. The splitting is accomplished by removing vertices of the graph at which an estimated density function of the data evaluates to values below a threshold. The density function is approximated on a sparse grid in order to make the method feasible in higher-dimensional settings and scalable in the number of data points. With benchmark examples we show that our method is competitive with other modern clustering methods. Furthermore, we consider a real-world example where we cluster nodes of a finite element model of a Chevrolet pick-up truck with respect to the displacements of the nodes during a frontal crash.

Original languageEnglish
Title of host publicationKI 2012
Subtitle of host publicationAdvances in Artificial Intelligence - 35th Annual German Conference on AI, Proceedings
Number of pages12
StatePublished - 2012
Event35th Annual German Conference on Artificial Intelligence, KI 2012 - Saarbrucken, Germany
Duration: 24 Sep 201227 Sep 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7526 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference35th Annual German Conference on Artificial Intelligence, KI 2012


  • clustering
  • density estimation
  • sparse grids


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