Abstract
One interesting way of accessing collections of multimedia objects is by methods of visualization and clustering. Growing self-organizing maps provide such a solution, which adapts automatically to the underlying database. Unfortunately, the result of the clustering greatly depends on the definition of the describing features and the used similarity measure. In this paper, we present a general approach to improve the obtained clustering by incorporating user feedback (in the form of drag-and-drop) into the underlying topology of the self-organizing map.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
T. Honkela, S. Kaski, K. Lagus, and T. Kohonen, Newsgroup Exploration with the WEBSOM Method and Browsing Interface, Technical Report, Helsinki University of Technology, Neural Networks Research Center, Espoo, Finland, 1996s.
T. Kohonen, Self-Organization and Associative Memory, Springer-Verlag, Berlin, 1984.
T. Kohonen, Self-Organized Formation of Topologically Correct Feature Maps, Biological Cybernetics, 43, pp. 59–69, 1982.
A. Klose, A. Nürnberger, R. Kruse, G. K. Hartmann, and M. Richards, Interactive Text Retrieval Based on Document Similarities, Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, 25(8), pp. 649–654, Elsevier Science, Amsterdam, 2000.
A. Narasimhalu, Special issue on content-based retrieval, ACM Multimedia Systems, 3(1), 1995.
A. Nürnberger and M. Detyniecki, Visualizing Changes in Data Collections Using Growing Self-Organizing Maps, Proceedings of the International Joint Conference on Neural Networks-IJCNN’2002, Honolulu, Hawaii, pp. 1912–1917, May, 2002.
A. Nürnberger, Interactive Text Retrieval Supported by Growing Self-Organizing Maps, In: T. Ojala (edt.), Proc. of the International Workshop on Information Retrieval (IR’2001), pp. 61–70, Infotech, Oulu, Finland, 2001.
A. Nürnberger and M. Detyniecki, Content Based Analysis of Email Databases Using Self-Organizing Maps, Proceedings of the European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems–EUNITE’2001, Tenerife, Spain, pp. 134–142, December, 2001.
A. Nürnberger and M. Detyniecki, User Adaptive Methods for Interactive Analysis of Document Databases, Proceedings of the European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems-EUNITE’2002, Algarve, Portugal, September, 2002.
A. Nürnberger, and A. Klose, Interactive Retrieval of Multimedia Objects based on Self-Organising Maps, In: Proc. of the Int. Conf. of the European Society for Fuzzy Logic and Technology (EUSFLAT 2001), pp. 377–380, De Montfort University, Leicester, UK, 2001.
A. Nürnberger and A. Klose, Improving Clustering and Visualization of Multimedia Data Using Interactive User Feedback, In: Proc. of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems-IPMU 2002, pp. 993–999, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nürnberger, A., Detyniecki, M. (2003). Weighted Self-Organizing Maps: Incorporating User Feedback. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_105
Download citation
DOI: https://doi.org/10.1007/3-540-44989-2_105
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40408-8
Online ISBN: 978-3-540-44989-8
eBook Packages: Springer Book Archive