Title: Reliable Data Mining in Uncertain Data
Speaker: Andreas Zuefle (George Mason University, USA)
When: 2020-01-30 16:15
Where: OH 14, E023
Our ability to extract knowledge from data is often impaired by unreliable, erroneous, obsolete, imprecise, sparse, and noisy data. Existing solutions for data mining often assume that all data are uniformly reliable and representative. Oblivious to sample size and sample variance, it is clear that mined patterns may be spurious, that is, caused by random variations rather than a causal signal. This is particularly problematic if latent features and deep learning methods are used to mine patterns, as their lack of interpretability prevents domain experts and decision makers from explaining spurious conclusions. This presentation will survey data mining algorithms that can exploit reliability information of data to enrich mined patterns with significance information. In detail, we will discuss the use of Monte Carlo and agent-based simulation to gain insights on the reliability of data mining results and we will look at applications for handling.
Forecasting Consumer Confidence in Germany via Twitter Data
Speaker: Deniz Karaman Örsal (Fakultät für Wirtschafts- und Sozialwissenschaften, Volkswirtschaftslehre, Universität Hamburg)
When and where?
Tuesday, April 28, 2020, 4.30 pm, M / E21