Campus Event Calendar
Event Entry
What and Who
Characteristics and Commonalities – Differentially Describing Datasets with Insight-ful Patterns
Sebastian Dalleiger
CISPA Helmholtz Center for Information Security
Promotionskolloquium
AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI
Public Audience
English
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Date, Time and Location
Thursday, 16 November 2023
14:00
60 Minutes
E1 4
024
Saarbrücken
Abstract
Empirical science revolves around gaining insights from complex data.
With the advent of computational science, increasingly more, larger, and
richer datasets are becoming avail-able to expand our scientific
knowledge. However, the analysis of these datasets by domain experts is
often impaired by a lack of suitable computational tools. In particular,
there is a shortage of methods identifying insightful patterns, i.e.,
sets of strongly associated feature values that are informative,
contrasting, probabilistically sound, statistically sound, and
discoverable using scalable algorithms. This thesis leverages ideas and
concepts from pat-tern-set mining, maximum-entropy modeling, statistical
testing, and matrix factorization to develop methods for discovering
insightful patterns.
With the advent of computational science, increasingly more, larger, and
richer datasets are becoming avail-able to expand our scientific
knowledge. However, the analysis of these datasets by domain experts is
often impaired by a lack of suitable computational tools. In particular,
there is a shortage of methods identifying insightful patterns, i.e.,
sets of strongly associated feature values that are informative,
contrasting, probabilistically sound, statistically sound, and
discoverable using scalable algorithms. This thesis leverages ideas and
concepts from pat-tern-set mining, maximum-entropy modeling, statistical
testing, and matrix factorization to develop methods for discovering
insightful patterns.
Contact
Petra Schaaf
+49 681 9325 5000
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Petra Schaaf, 11/10/2023 14:23 -- Created document.