scholarly journals Modified Adaptive Resonance Theory Network for Mixed Data Based on Distance Hierarchy

2006 ◽  
pp. 757-764 ◽  
Author(s):  
Chung-Chian Hsu ◽  
Yan-Ping Huang ◽  
Chieh-Ming Hsiao
Keyword(s):  
Adaptive Resonance Theory ◽  
Mixed Data ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Adaptive Resonance Theory Network

Tree-ART2 Learning Model for Spatial Clustering in Second Dimension

2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao
Keyword(s):  
Network Model ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Adaptive Resonance Theory ◽  
Spatial Distance ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Vector Module

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.

WIS-ART: UNSUPERVISED CLUSTERING WITH RAM DISCRIMINATORS

1992 ◽  
Vol 03 (01) ◽  
pp. 57-63 ◽  
Author(s):  
Eamon P. Fulcher
Keyword(s):  
Random Access ◽  
Test Phase ◽  
Adaptive Resonance Theory ◽  
The Self ◽  
Hierarchical Level ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Pattern Clustering ◽  
Adaptive Pattern ◽  
System Operating

WIS-ART merges the self-organising properties of Adaptive Resonance Theory (ART) with the operation of WISARD, an adaptive pattern recognition machine which uses discriminators of conventional Random Access Memories (RAMs). The result is an unsupervised pattern clustering system operating at near real-time that implements the leader algorithm. ART’s clustering is highly dependent upon the value of a “vigilance” parameter, which is set prior to training. However, for WIS-ART hierarchical clustering is performed automatically by the partitioning of discriminators into “multi-vigilance modules”. Thus, clustering may be controlled during the test phase according to the degree of discrimination (hierarchical level) required. Methods for improving the clustering characteristics of WIS-ART whilst still retaining stability are discussed.

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