  In streaming data applications incoming samples are processed and discarded,
therefore, intelligent decision-making is crucial for the performance of
lifelong learning systems. In addition, the order in which samples arrive may
heavily affect the performance of online (and offline) incremental learners.
The recently introduced incremental cluster validity indices (iCVIs) provide
valuable aid in addressing such class of problems. Their primary use-case has
been cluster quality monitoring; nonetheless, they have been very recently
integrated in a streaming clustering method to assist the clustering task
itself. In this context, the work presented here introduces the first adaptive
resonance theory (ART)-based model that uses iCVIs for unsupervised and
semi-supervised online learning. Moreover, it shows for the first time how to
use iCVIs to regulate ART vigilance via an iCVI-based match tracking mechanism.
The model achieves improved accuracy and robustness to ordering effects by
integrating an online iCVI framework as module B of a topological adaptive
resonance theory predictive mapping (TopoARTMAP) -- thereby being named
iCVI-TopoARTMAP -- and by employing iCVI-driven post-processing heuristics at
the end of each learning step. The online iCVI framework provides assignments
of input samples to clusters at each iteration in accordance to any of several
iCVIs. The iCVI-TopoARTMAP maintains useful properties shared by ARTMAP models,
such as stability, immunity to catastrophic forgetting, and the many-to-one
mapping capability via the map field module. The performance (unsupervised and
semi-supervised) and robustness to presentation order (unsupervised) of
iCVI-TopoARTMAP were evaluated via experiments with a synthetic data set and
deep embeddings of a real-world face image data set.
