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STORM - A Novel Information Fusion and Cluster Interpretation Technique

  • Jan Feyereisl
  • Uwe Aickelin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)

Abstract

Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.

Keywords

Expert Knowledge Information Fusion Exploratory Data Analysis Reference Vector Cluster Boundary 
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 2009

Authors and Affiliations

  • Jan Feyereisl
    • 1
  • Uwe Aickelin
    • 1
  1. 1.School of Computer ScienceThe University of NottinghamUK

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