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Vorlesungsverzeichnis >> Medizinische Fakultät (Med) >>
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Bayesian Rule Mining (ACR)- Dozentinnen/Dozenten
- Prof. Dr. Oliver Amft, Dr. rer. nat. Luis Ignacio Lopera Gonzales
- Angaben
- Seminar
, benoteter Schein, Anwesenheitspflicht, ECTS-Studium, ECTS-Credits: 5, Sprache Englisch
Zeit und Ort: n.V.; Bemerkung zu Zeit und Ort: First meeting: MVC, Henkestr. 91, Bldg. 7, 1st Floor, 373
Vorbesprechung: 16.10.2019, 17:00 - 18:00 Uhr
- ECTS-Informationen:
- Credits: 5
- Prerequisites
- Useful knowledge: Python, data analyt
- Contents
- Background: The field of knowledge discovery has focused on the extraction of rules created from the frequent association symbols. With the recent introduction of the Bayesian rule mining algorithms, the focus is shifting from measuring relevance based on rule frequency and moving towards the increasing belief concept.
Regardless of the rule relevance metric of choice, rule mining poses the secondary challenge of finding rules’ premise and conclusion symbol-sets.
Aim: Transform some of the techniques used to find frequent symbol sets, into finding symbol sets that have the increasing belief property. Specifically, we will explore the use of genetic algorithms to find rules that have increasing belief.
Learning objectives: Gain an overview of rule mining frameworks. Understand concepts of data processing, generating process distributions and relationships. Apply mining frameworks, and compare
frequent rule mining methodologies to Bayesian rule mining methods. Create a Bayesian data miner that uses genetic algorithms.
- Literature
- Up-to-date literature recommendations will be provided during the seminar.
- Zusätzliche Informationen
- Schlagwörter: ACR
Erwartete Teilnehmerzahl: 10, Maximale Teilnehmerzahl: 20
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt von Mittwoch, 4.9.2019, 08:00 Uhr bis Dienstag, 15.10.2019, 18:00 Uhr über: StudOn.
- Institution: Lehrstuhl für Digital Health
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