  This paper presents a novel adaptive resonance theory (ART)-based modular
architecture for unsupervised learning, namely the distributed dual vigilance
fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local
fuzzy ART modules. It is equipped with the distinctive features of distributed
higher-order activation and match functions, using dual vigilance parameters
responsible for cluster similarity and data quantization. Together, these allow
DDVFA to perform unsupervised modularization, create multi-prototype clustering
representations, retrieve arbitrarily-shaped clusters, and control its
compactness. Another important contribution is the reduction of
order-dependence, an issue that affects any agglomerative clustering method.
This paper demonstrates two approaches for mitigating order-dependence:
preprocessing using visual assessment of cluster tendency (VAT) or
postprocessing using a novel Merge ART module. The former is suitable for batch
processing, whereas the latter can be used in online learning. Experimental
results in the online learning mode carried out on 30 benchmark data sets show
that DDVFA cascaded with Merge ART statistically outperformed the best other
ART-based systems when samples were randomly presented. Conversely, they were
found to be statistically equivalent in the offline mode when samples were
pre-processed using VAT. Remarkably, performance comparisons to non-ART-based
clustering algorithms show that DDVFA (which learns incrementally) was also
statistically equivalent to the non-incremental (offline) methods of DBSCAN,
single linkage hierarchical agglomerative clustering (HAC), and k-means, while
retaining the appealing properties of ART. Links to the source code and data
are provided. Considering the algorithm's simplicity, online learning
capability, and performance, it is an ideal choice for many agglomerative
clustering applications.
