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.
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Herrmann, L., Ultsch, A. (2008). An Artificial Life Approach for Semi-supervised Learning. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_17
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DOI: https://doi.org/10.1007/978-3-540-78246-9_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78239-1
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