Publication - Clustering with Artificial Neural Networks and Traditional Techniques.
RESEARCH

Clustering with Artificial Neural Networks and Traditional Techniques.

Research Area:  
Other topics in Computer Science
    
Type:  
Journal article

 

Year: 2003
Authors: George Tambouratzis; T. Tambouratzis; D. Tambouratzis
Journal: International Journal of Intelligent Systems
Volume: 18
Number: 4
Pages: 405-428
DOI: 10.1002/int.10095
Abstract:
In this article, two clustering techniques based on neural networks are introduced. The two neural network models are the Harmony theory network (HTN) and the self-organizing logic neural network (SOLNN), both of which are characterized by parallel processing, a distributed architecture, and a large number of nodes. After describing their clustering characteristics and potential, a comparison to classical statistical techniques is performed. This comparison allows the creation of a correspondence between each neural network clustering technique and particular metrics as used by the corresponding statistical methods, which reflect the affinity of the clustered patterns. In particular, the HTN is found to perform the clustering task with an accuracy similar to the best statistical methods, while it is further capable of proposing an optimal number of groups into which the patterns may be clustered. On the other hand, the SOLNN combines a high clustering accuracy with the ability to cluster higher-dimensional patterns without a considerable increase in the processing time.
[Bibtex]