Conceptual Knowledge Discovery
Data Mining with Formal Concept
Analysis
Tutorial by Gerd Stumme
ECML/PKDD 2002
Helsinki, Finland
August 19-23,
2002
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Formal Concept Analysis is an
unsupervised learning technique for discovering conceptual structures in of
data. These structures are graphically represented as conceptual hierarchies,
allowing the analysis of complex structures and the discovery of dependencies within
the data. Formal Concept Analysis is a conceptual clustering technique applied
in data analysis, information retrieval, and knowledge discovery; and has
received increasing attention in the KDD community during the last years.
Formal Concept Analysis arose twenty years ago as a theory for the formalization
of the concept of ‘concept'. It is based on the philosophical understanding
that a concept is constituted by two parts: its extension which consists of all
objects belonging to the concept, and its intension which comprises all
attributes shared by those objects. This understanding – which is also reflected
by the international standard ISO 704 – allows to derive all concepts from a given context (data table)
and to introduce a subsumption hierarchy.
The tutorial provides an introduction into Formal Concept Analysis and
discusses its applications in KDD. In order to get experience with this
instrument of analysis, the participants will do practical training on given
data stocks.
The tutorial is suitable to the general
audience of both ECML and PKDD. It is of interest to theoreticians and
practitioners from data analysis, machine learning, information retrieval, data
mining, knowledge discovery, and the general AI audience interested
in this increasing research area.
The tutorial aims at introducing researchers and practitioners of related
fields, as well as the general ECML/PKDD audience, to Formal Concept Analysis. It
surveys theory and applications of Formal Concept Analysis, focussing on its
applications in KDD.
10 min. Introduction
40 min. Formal Contexts & Concept Lattices
10 min Application Examples I
20 min. Computing Concept Lattices
30 min. Exercises
20 min. Coffee Break
40 min. Conceptual Clustering
30 min. Exercises
20 min. FCA-Based Mining of Association Rules
20 min Application Examples II
Resume of Gerd Stumme
Dr. Gerd Stumme
Institut
für Angewandte Informatik und
Formale Beschreibungsverfahren
(AIFB)
Universität Karlsruhe (TH)
D-76128 Karlsruhe
Germany
http://www.aifb.uni-karlsruhe.de/WBS/gst/
Gerd Stumme is senior researcher at the Institute of Applied Informatics
and Formal Description Methods (AIFB) at the University of Karlsruhe. He
received his PhD from Darmstadt University of Technology, where he co-worked
for several years with Rudolf Wille, the founder of
Formal Concept Analysis. Gerd Stumme published over 40 papers on Formal Concept
Analysis. He also chaired several conferences about Formal Concept Analysis.