Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multicategorical symbolic data. This article proposes Symbolic Tree Adaptive Resonance Theory (START), with additional supervised, Dual-Vigilance (DV-START), and Distributed Dual-Vigilance (DDV-START) formulations, for the clustering of multicategorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot-Marie-Tooth disease using genomic, phenotypic, and proteomic data. AUTHORS NOTE: this article outlines the Symbolic Tree Adaptive Resonance Theory (START) machine learning algorithm, which is unrelated to the similarly named Spectral Timing Adaptive Resonance Theory (START) explanatory neural network model.

Context recognition for lifelong learning (L2) agents is an open-ended problem whereby aggregate features in an environment are utilized to signal the active context in which the agent is operating. The ability to recognize context is necessary in L2 agents to engage modulatory signals to account for significant changes in the input state space associated with a given context or task, such as altering learning dynamics or shifting attention to more relevant features. Context recognition is itself an L2 problem due to the ever-increasing number of distinct contexts that an agent might encounter, requiring incrementally learning novel contexts while prescribing them to supervised task labels when available. This paper demonstrates an algorithm based on near clustering of deep-extracted features with adaptive resonance theory methods that satisfies these requirements on the behalf of an embodied L2 agent in a computer vision environment. The strength of this algorithm lies in its flexibility, being capable of online incremental learning in supervised, realistic semi-supervised, and unsupervised scenarios while demonstrating continual learning in its own right.

Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in improving unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta-iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Some iCVIs are better at detecting under-partition; others at over-partition. Combining them was hypothesized to improve cluster validation analysis. Experiments were conducted on generalized synthetic and real-world data sets to demonstrate the efficacy and application of this method. Results of 100% accuracy were achieved in labeling partition quality on real-world data sets including MNIST and FLIR ADAS, demonstrating that the Meta-iCVI is a powerful and efficient tool for classifying partition quality in a variety of conditions. Its introduction should empower new and more efficient streaming clustering techniques. Additionally, we believe this to be the first implementation of an ensemble iCVI metric and the first time iCVI validation performance has been evaluated on randomized sample presentation.