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Cited by 3Distributed systems and fault toleranceRadiation Effects in ElectronicsAdvanced Clustering Algorithms Research

Resilient K-d Trees: K-Means in Space Revisited

Gieseke, Fabian, Moruz, Gabriel, Vahrenhold, Jan·2010·2010 IEEE International Conference on Data Mining
DOI 10.1109/icdm.2010.94
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Abstract

We develop a k-d tree variant that is resilient to a pre-described number of memory corruptions while still using only linear space. We show how to use this data structure in the context of clustering in high-radiation environments and demonstrate that our approach leads to a significantly higher resiliency rate compared to previous results.

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  • Even more recent spacecraft run on processors and memory that lag a decade behind desktop state of the art.

    “This implies that even more recent spacecraft run on processors and memory that lag a decade behind the desktop state of the art [235].”

    From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [235]

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Authors
Gieseke, F., Moruz, G., & Vahrenhold, J.
Affiliations
  • Carl von Ossietzky Universität Oldenburg (DE)
  • Goethe University Frankfurt (DE)
  • TU Dortmund University (DE)
ORCID
Venue
2010 IEEE International Conference on Data Mining
Publisher
IEEE
Published
2010-12
Pages
815-820
ISSN
1550-4786 (ISSN-L)
DOI
10.1109/icdm.2010.94
OpenAlex
W2123199979
Cited by
3
References
0
Citations by year
2021: 1 · 2012: 1
FWCI
0.58
Type
conference paper
Language
en
Open access
closed
Locations
Proceedings (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Radiation Effects in Electronics
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
Keywords
k-d tree, memory corruption, K-means clustering, high radiation environment, vector spaces, fault tolerance

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