Oberseminar (SS 2019)

• (B) Temporal Cycles in Event Data
• (A) Label-Independent Process Matching
• (P) Pixelweise Segmentierung von OCT Bildern

• (A) Bayesian Convolutional Neural Networks
• (A) Prädiktive Ausfallerkennung am Prüfstand
• (M) EFISC - An Extendable Framework for Interactive Subspace-Clustering
• (A) A kNN-based Clustering Approach

• (A) Comparison of Different Grid Construction Methods in Clustering
• (A) Angular-Based Clustering
• (A) Trans-Subspace Correlations between Multi-Resolution Periodic Parameter Spaces
• (A) Interactive Spectral Clustering

• (A) Relation Disambiguation using RESCAL
• (A) Routing: Replanning vs Stochastic Planning
• (A) Simulation of a Stochastic Environment with Agent Interaction for Reinforcement Learning Tasks

• (B) Application of Data Mining Methods for Customer Clustering
• (A) Knowledge Graph Fusion using Cycle Consistency

• (A) On Clustering in Linear Parameter Space Through Sampled Functions
• (A) GPU grid-based piecewise linear approximation for linearly correlated clusters
• (A) Clustering of temporally distorted frame segmentation and assembly on video data
• (B) Knowledge Graph Representations of Event Logs

• (A) On Quality Metric Ensembles in Context of Hierarchical Clustering
• (A) Interactive Hierarchical Clustering
• (A) Reference-Model-Based Distances on Trace Data
• (A) k-dense Clustering
• (A) A MapReduce Variant of 4C

• (B) Fact Prediction on Knowledge Graphs using Debate Dynamics
• (A) Improving DBSCAN using k-Core Decomposition
• (A) Time Improvements of kProcess
• (B) k-Nearest Neighbor Based Clustering

• (A) kNN in High- Dimensional Data
• (A) Variants of Spectral Clustering
• (A) Bottom-Up Subspace Clustering using kNN-Graph Similiarity
• (A) Negative Sampling Strategies for Link Prediction in Knowledge Graphs
• (A) Interactive Parameter Space Transform

• (M) A kNN-based Importance Score for Subspaces
• (A) Process Model Decomposition with Infomap
• (A) Coalition-based Ranking on Process Models
• (A) Neural End-To-End Subspace Clustering

• (M) Comparison between different Deep Learning Architectures for Object Detection in an Industrial Environment
• (A) Partially Observable Resource Routing with Reinforcement Learning
• (A) Time Series Analysis for the Detection of Failures of ALMA Space Telescopes

• (A) Detecting Global Correlated Clusters using Hough Transform through Locally Dense Correlations
• (B) Eine dynamische webbasierte Benutzeroberfläche und ein DAG Generator für das Zitan Projekt
• (B) Graphische Oberfläche für Zitan: Implementierung einer Spring Application mit DSL zur Generierung des Domain Models

• (A) Active Learning For Entity Alignment

• (B) Interactive Hierarchical Clustering
• (M) Generative Adversarial Networks for Multi-Modal Distributions
• (M) Anytime Outlier Detection based on Distributions and k-Means

11.09.2019
10-12
room 151

• (M) Prädiktive Ausfallerkennung am Prüfstand
• (M) Nonlinear k-Distance Approximation

12.09.2019
10-12
room 151

• (B) Time Improvements of kProcess
• (B) Bottom-Up Subspace Clustering using kNN-Graph Similiarity

24.09.2019
10-12
room 151

• (B) Travelling Officer Problem mit Ant Colony Optimization und Monte Carlo Tree Search
• (M) Map Fusion Using Graph Convolutional Networks
• (B) Spatio-Temporal Link Prediction

24.09.2019
14-16
room 151

• (M) Neural End-To-End Subspace Clustering
• (B) Reference-Model-Based Distances on Trace Data
• (B) Angle-Based Clustering

• (B) Interactive Spectral Clustering
• (M) A kNN-based Anytime Clustering Approach
• (B) Clustering for Linear Trajectories in Video Data

• (B) Routing: Replanning vs Stochastic Planning
• (B) Comparison of Different Grid Construction Methods in Clustering
• (B) Anomaly Detection on Nested Event Stream