Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II
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Article
Open AccessRotation invariant GPS trajectory mining
Mining of GPS trajectories of moving vehicles and devices can provide valuable insights into urban systems, planning and operational applications. Understanding object motion often requires that the spatial-te...
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Book and Conference Proceedings
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Book and Conference Proceedings
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part I
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Book and Conference Proceedings
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part IV
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Book and Conference Proceedings
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III
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Book and Conference Proceedings
Machine Learning and Knowledge Discovery in Databases: Research Track
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part V
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Chapter and Conference Paper
Poisson Graphical Granger Causality by Minimum Message Length
Graphical Granger models are popular models for causal inference among time series. In this paper we focus on the Poisson graphical Granger model where the time series follow Poisson distribution. We use minim...
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Chapter and Conference Paper
Utilizing Structure-Rich Features to Improve Clustering
For successful clustering, an algorithm needs to find the boundaries between clusters. While this is comparatively easy if the clusters are compact and non-overlap** and thus the boundaries clearly defined, ...
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Article
Open AccessDeepECT: The Deep Embedded Cluster Tree
The idea of combining the high representational power of deep learning techniques with clustering methods has gained much attention in recent years. Optimizing a clustering objective and the dataset representa...
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Article
Open AccessClustering of mixed-type data considering concept hierarchies: problem specification and algorithm
Most clustering algorithms have been designed only for pure numerical or pure categorical data sets, while nowadays many applications generate mixed data. It raises the question how to integrate various types ...
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Article
The Data Mining Group at University of Vienna
How can we extract meaningful knowledge from massive amounts of data? The data mining group at University of Vienna contributes novel methods for exploratory data analysis. Our main research focus is on unsupe...
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Article
Open AccessDataset-Transformation: improving clustering by enhancing the structure with DipScaling and DipTransformation
A data set might have a well-defined structure, but this does not necessarily lead to good clustering results. If the structure is hidden in an unfavourable scaling, clustering will usually fail. The aim of th...
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Chapter and Conference Paper
ITGH: Information-Theoretic Granger Causal Inference on Heterogeneous Data
Granger causality for time series states that a cause improves the predictability of its effect. That is, given two time series x and y, we are interested in detecting the causal relations among them considering...
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Chapter and Conference Paper
RandomLink – Avoiding Linkage-Effects by Employing Random Effects for Clustering
We present here a new parameter-free clustering algorithm that does not impose any assumptions on the data. Based solely on the premise that close data points are more likely to be in the same cluster, it can ...
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Chapter and Conference Paper
Granger Causality for Heterogeneous Processes
Discovery of temporal structures and finding causal interactions among time series have recently attracted attention of the data mining community. Among various causal notions graphical Granger causality is we...
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Chapter and Conference Paper
Clustering of Mixed-Type Data Considering Concept Hierarchies
Most clustering algorithms have been designed only for pure numerical or pure categorical data sets while nowadays many applications generate mixed data. It arises the question how to integrate various types o...
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Chapter and Conference Paper
KMN - Removing Noise from K-Means Clustering Results
K-Means is one of the most important data mining techniques for scientists who want to analyze their data. But K-Means has the disadvantage that it is unable to handle noise data points. This paper proposes a ...
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Reference Work Entry In depth
High-Dimensional Indexing
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Chapter and Conference Paper
Parameter Free Mixed-Type Density-Based Clustering
Nowadays many applications generate mixed data objects consisting of numerical and categorical attributes. Simultaneously dealing with mixed objects is more challenging and various approaches convert one type ...
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Article
Synchronization-based scalable subspace clustering of high-dimensional data
How to address the challenges of the “curse of dimensionality” and “scalability” in clustering simultaneously? In this paper, we propose arbitrarily oriented synchronized clusters (ORSC), a novel effective and...
