Visual analytics for spatial clustering: Using a heuristic approach for guided exploration

Eli Packer, Peter Bak, Mikko Nikkila, Valentin Polishchuk, Harold J. Ship

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

We propose a novel approach of distance-based spatial clustering and contribute a heuristic computation of input parameters for guiding users in the search of interesting cluster constellations. We thereby combine computational geometry with interactive visualization into one coherent framework. Our approach entails displaying the results of the heuristics to users, as shown in Figure 1, providing a setting from which to start the exploration and data analysis. Addition interaction capabilities are available containing visual feedback for exploring further clustering options and is able to cope with noise in the data. We evaluate, and show the benefits of our approach on a sophisticated artificial dataset and demonstrate its usefulness on real-world data.

Original languageEnglish
Article number6634158
Pages (from-to)2179-2188
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume19
Issue number12
DOIs
StatePublished - 4 Nov 2013
Externally publishedYes

Keywords

  • Heuristic-based spatial clustering
  • iInteractive visual clustering
  • k-order a-(alpha)-shapes

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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