Tutorial 1: Formal Concept Analysis and Knowledge Discovery
Amedeo Napoli
Abstract:
This tutorial on Formal Concept Analysis (FCA) will make precise how FCA and extensions, i.e. Pattern Structures and Relational Concept Analysis (RCA), can be used for data processing and knowledge discovery. FCA provides a complete framework with well founded, efficient and practical algorithms, for dealing with heterogeneous and complex data in various application domains.
[Slides]
Tutorial 2: Mathematical Similarity Models
Moritz Schubert and Dominik Endres
Abstract:
Similarity is a fundamental aspect of human experience, e.g. when trying to comprehend a novel situation we search our memory for similar experiences to learn from. This workshop will give a historical overview of psychological similarity research by explaining four of the most relevant types of models in this field: Geometric models conceptualise similarity as metric distances between objects, featural models calculate similarity based on the intersections and differences of feature sets representing the objects, for structural models similarity is the degree to which structural representations of objects align with one another and transformational models propose similarity to be the number of transformations needed to transform one object into the other. We will go over a concrete model for each school of thought and discuss the pros and cons of the different approaches. A special focus will be on the question of how to find a good input data for the models, i.e. fitting descriptions of the subjects’ mental object representations.
[Slides]