Clustering MBTI Personalities With Graph Filters And Self Organizing Maps Over Pinecone

Self organizing maps (SOMs) or cognitive maps are designed to preserve major topological attributes of manifolds in a higher dimensionality data space to corresponding projections thereof in a low dimensional coordinate space. This is performed by mapping neighborhoods and the distances contained therein from the data space to ones in the coordinate space. Thus, SOM functionality relies heavily on the geometrical properties of both spaces. Topologically flexible data space distance metrics are constructed by combining tensors with graph filters, the latter coming from graph signal processing. The power of these distance metrics comes from naturally expressing the higher order relationships between points of the data space. This paves the way for addressing engineering scenarios involving a large number of densely interrelated attributes. One such case is discerning personalities from textual information based on the Myers-Briggs taxonomy indicator (MBTI), a framework of archetypal personalities derived from Jungian psychodynamic theory. Various graph filters were tested on a benchmark Kaggle dataset with ground truth with comparisons assessed in terms of topological error, cluster purity, average inter-cluster distance, and cluster curvature variability. Data points were stored in Pinecone, a recent vector database, with Python integration.