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An Artificial Life Approach for Semi-supervised Learning

  • Lutz Herrmann
  • Alfred Ultsch
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

An approach for the integration of supervising information into unsupervised clustering is presented (semi supervised learning). The underlying unsupervised clustering algorithm is based on swarm technologies from the field of Artificial Life systems. Its basic elements are autonomous agents called Databots. Their unsupervised movement patterns correspond to structural features of a high dimensional data set. Supervising information can be easily incorporated in such a system through the implementation of special movement strategies. These strategies realize given constraints or cluster information. The system has been tested on fundamental clustering problems. It outperforms constrained k-means.

Keywords

Autonomous Agent Input Sample Unlabeled Data Information Processing Technique Unsupervised Cluster 
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

  • Lutz Herrmann
    • 1
  • Alfred Ultsch
    • 1
  1. 1.Databionics Research GroupPhilipps-University MarburgGermany

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