Obtaining Shape Descriptors from a Concave Hull-Based Clustering Algorithm

  • Christian BrauneEmail author
  • Marco Dankel
  • Rudolf Kruse
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)


In data analysis clustering is one of the core processes to find groups in otherwise unstructured data. Determining the number of clusters or finding clusters of arbitrary shape whose convex hulls overlap is in general a hard problem. In this paper we present a method for clustering data points by iteratively shrinking the convex hull of the data set. Subdividing the created hulls leads to shape descriptors of the individual clusters. We tested our algorithm on several data sets and achieved high degrees of accuracy. The cluster definition employed uses a notion of spatial separation. We also compare our algorithm against a similar algorithm that automatically detects the boundaries and the number of clusters. The experiments show that our algorithm yields the better results.


Density based clustering Convex hulls Concave hulls Noise removal Automatic detection of cluster number 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Otto-von-Guericke-University of MagdeburgMagdeburgGermany

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