Temporal Relational Concept Analysis (TRCA) – Full-day tutorial (Tuesday)
Speaker: Karl Erich Wolff
Abstract: The tutorial leads the participants in an interactive atmosphere from their intuitive understanding of temporal or relational data to a theoretically well-founded conceptual representation of temporal relational data with relations of arbitrary arity. Starting from small examples of temporal data we first introduce the main ideas around the notion of a temporal system and a state of an object at a certain time. In the most simple case that leads to trajectories of objects, in the general case of distributed objects to a clear conceptual understanding of the problems around particles and waves in physics. In a second step we extent the temporal data to temporal relational data with relations of arbitrary arity. In the afternoon session the tutorial will focus on conceptual scaling using the program Cernato, then on the Temporal Concept Analysis Tool in Siena, and finally on the representation of moving distributed concepts in ToscanaJ.
More information here.
Preference-based Pattern Mining – Half-day tutorial (Tuesday morning)
Speakers: Bruno Crémilleux, Marc Plantevit and Arnaud Soulet
Abstract: This tutorial focuses on the recent shift from constraint-based pattern mining to preference-based pattern mining and interactive pattern mining. Constraint-based pattern mining, which shares common notions with FCA, is now a mature domain of data mining that makes it possible to handle various different pattern domains (e.g., itemsets, sequences, graphs) with a large variety of constraints thanks to solid theoretical foundations and an efficient algorithmic machinery. Even though, it has been realized for a long time that it is difficult for the end-user to model her interest in term of constraints and above to overcome the well-known thresholding issue, researchers have only recently intensified their study of methods for finding high-quality patterns according to the user’s preferences. In this tutorial, we discuss the need of preferences in pattern mining, the principles and methods of the use of preferences in pattern mining. Many methods are derived from constraint-based pattern mining by integrating utility functions or interestingness measures as quantitative preference model. This approach transforms pattern mining in an optimization problem guided by user specified preferences. However, in practice, the user has only a vague idea of what useful patterns could be. The recent research field of interactive pattern mining relies on the automatic acquisition of these preferences and the development of the instant data mining field.
More information here.
Logic and FCA via Management Science – Half-day tutorial (Tuesday afternoon)
Speaker: Alessandra Palmigiano
Abstract: There is a clear affinity between logic and FCA, but so far, the research agendas of the two fields have remained mostly separated. This tutorial attempts at outlining a systematic connection between the two fields. This connection is conceptually motivated by categorization theory as is developed in management science, and this conceptual motivation is perhaps an added value to this project, given that management science is not an established field of application for logic nor for FCA, and this three-sided connection opens many research paths.
More information here.
