Patch Relational Neural Gas – Clustering of Huge Dissimilarity Datasets

  • Alexander Hasenfuss
  • Barbara Hammer
  • Fabrice Rossi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5064)


Clustering constitutes an ubiquitous problem when dealing with huge data sets for data compression, visualization, or preprocessing. Prototype-based neural methods such as neural gas or the self-organizing map offer an intuitive and fast variant which represents data by means of typical representatives, thereby running in linear time. Recently, an extension of these methods towards relational clustering has been proposed which can handle general non-vectorial data characterized by dissimilarities only, such as alignment or general kernels. This extension, relational neural gas, is directly applicable in important domains such as bioinformatics or text clustering. However, it is quadratic in m both in memory and in time (m being the number of data points). Hence, it is infeasible for huge data sets. In this contribution we introduce an approximate patch version of relational neural gas which relies on the same cost function but it dramatically reduces time and memory requirements. It offers a single pass clustering algorithm for huge data sets, running in constant space and linear time only.


Patch Size Quantization Error Dissimilarity Matrix Batch Optimization Relational Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alexander Hasenfuss
    • 1
  • Barbara Hammer
    • 1
  • Fabrice Rossi
    • 2
  1. 1.Department of InformaticsClausthal University of TechnologyClausthal-ZellerfeldGermany
  2. 2.Projet AxIS, INRIA Rocquencourt, Domaine de Voluceau, RocquencourtLe Chesnay CedexFrance

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