ECDA 2019 - European Conference on Data Analysis
Bayreuth, Germany, 18-20 March
Statistical and machine learning can significantly speed up human knowledge development, helping to determine the basic categories in a relatively short amount of time. Exploratory data analysis (EDA) can be considered the forefather of statistical learning; it relies on the mind’s ability to learn from data and, in particular, it aims to summarize data-sets through a limited number of interpretable latent features or clusters offering cognitive geometric models to define categorizations. It can also be understood as the implementation of the human cognitive process extended to huge amounts of data: Big Data. But EDA alone cannot answer to the questions: “How many, and what are the categories to retain?” and “What are the observations that can represent a category better than others in human cognitive processes?”. The concept of categorization implies data summarization in a limited number of well-separated groups that must be maximally and internally homogeneous at the same time. This contribute aims to present an overview of the most recent literature on the archetypal analysis (AA) and its related methods as a statistical categorization approach. At the same time some most recent approaches in prototypes identification when dealing with large and huge data-sets are presented. In combination with consistent clustering approaches, AA helps to identify those observed or unobserved prototypes that satisfy Rosch’s definition. Those small number of groups that are maximally homogeneous within the units belonging to the same group and maximally heterogeneous among groups, and which allow the development of the human knowledge by the relationships between prototypes and a new unknown object.