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Recursive self-organizing network models.

Barbara Hammer¹, Alessio Micheli, Alessandro Sperduti

¹Research Group LNM, Department of Mathematics/Computer Science, University of Osnabrück, Albrechtstrasse 28, Osnabrück D-49069, Germany. hammer@informatik.uni-osnabrueck.de

Neural Networks : the Official Journal of the International Neural Network Society

|November 24, 2004

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This study reviews self-organizing models for data mining, extending them for sequential and tree data. Experiments show their effectiveness in processing complex structures and preserving topology.

Area of Science:

Computational intelligence
Machine learning
Data mining

Background:

Self-organizing models are crucial for data visualization, clustering, and data mining.
Existing models often struggle with sequential and tree-structured data.

Purpose of the Study:

To provide a unified review of advanced self-organizing models.
To investigate the mathematical properties and capabilities of these models.
To compare different approaches through experimental analysis.

Main Methods:

Extension of basic vector-based models using recursive computation.
Development of a general framework encompassing supervised recurrent and recursive models.
Theoretical analysis of internal structure representation and similarity measures.

Related Experiment Videos

Experimental comparison using time series and tree-structured data.

Main Results:

Demonstrated capability of recursive models to process sequential and tree-structured data.
Formalized internal structure storage and induced similarity measures.
Experimental validation of representational capabilities, topology preservation, and noise tolerance.
Successful application to time series and complex tree data.

Conclusions:

Recursive self-organizing models offer a powerful framework for diverse data types.
These models exhibit strong representational capabilities and robustness.
The unified framework facilitates understanding and comparison of various approaches.