The author reviews the theory and practice of determining what parts of a data set are ultrametric. He de- scribes the potential relevance of ultrametric topology as a framework for unconscious thought processes. This view of ultrametric topology as a framework that complements metric-based, conscious, Aristotelian logical reasoning comes from the work of the Chilean psychoanalyst, Ignacio Matte Blanco. Taking text data, the author develops an algorithm for finding local ultrametricity in such data. He applies that in two case studies. The first relates to a large set of dream reports, and therefore can possibly recall traces of unconscious thought processes. The second case study uses Twitter social media, and has the aim of picking up underlying associations. The author’s case studies are selective in regard to names of people and objects, and are focused in order to highlight the principle of his approach, which is one of particular pattern finding in textual data.
Keywords: Cognition, Computation, Correspondence Analysis, Hierarchical Clustering, Metric, Multivariate Data Analysis, Psychoanalysis, Social Media, Text Analysis, Ultrametric, Unsupervised Classification
Fionn Murtagh, Department of Computing and Mathematics, University of Derby, Derby, UK & Department of Computing, Goldsmiths University of London, London, UK
International Journal of Cognitive Informatics and Natural Intelligence, 8(4), 1-16, October-December 2014 1