A study on exploiting VAT to mitigate ordering effects in Fuzzy ART

The clustering structures formed by Adaptive Resonance Theory (ART) and many other algorithms are dependent on input presentation/permutation order. In this work, we exploit Visual Assessment of cluster Tendency (VAT) as a pre-processor for Fuzzy ART in order to mitigate this problem. This approach is a global strategy that uses similarity-based ordering before clustering. Experimental results show that this framework improved peak and average performance, reduced the number of categories, and incurred less variability in the clustering outcome. By enhancing performance and reducing sensitivity to input order presentation, this approach is recommended when it is suitable to perform off-line incremental learning.