What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Database
Peer Kröger mostly deals with Artificial intelligence, Pattern recognition, Data mining, Cluster analysis and Correlation clustering.
The concepts of his Artificial intelligence study are interwoven with issues in Algorithm and Linear subspace.
He has researched Algorithm in several fields, including Cover tree and Robustness.
His work on k-nearest neighbors algorithm, Nearest neighbor search, Large margin nearest neighbor and Nearest neighbor graph as part of general Pattern recognition study is frequently linked to Metric, bridging the gap between disciplines.
The study incorporates disciplines such as Data science, Principal component analysis and Data set in addition to Data mining.
Fuzzy clustering, CURE data clustering algorithm, Clustering high-dimensional data and DBSCAN are the primary areas of interest in his Cluster analysis study.
His most cited work include:
- Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering (906 citations)
- Density‐based clustering (417 citations)
- LoOP: local outlier probabilities (300 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Data mining, Cluster analysis, Artificial intelligence, Pattern recognition and Correlation clustering.
His Data mining research is multidisciplinary, relying on both Hierarchical clustering, Algorithm, Theoretical computer science and k-nearest neighbors algorithm.
His Cluster analysis research focuses on Subspace topology and how it connects with Linear subspace.
His research on Artificial intelligence often connects related areas such as Machine learning.
His Pattern recognition research also works with subjects such as
- Curse of dimensionality which is related to area like Search engine indexing,
- Outlier that connect with fields like Anomaly detection.
His Correlation clustering study typically links adjacent topics like Fuzzy clustering.
He most often published in these fields:
- Data mining (42.76%)
- Cluster analysis (32.89%)
- Artificial intelligence (28.95%)
What were the highlights of his more recent work (between 2016-2021)?
- Cluster analysis (32.89%)
- Artificial intelligence (28.95%)
- Pattern recognition (22.37%)
In recent papers he was focusing on the following fields of study:
Peer Kröger mainly focuses on Cluster analysis, Artificial intelligence, Pattern recognition, Data mining and Nearest neighbor search.
His Cluster analysis research integrates issues from Subspace topology, Hough transform and Linear subspace.
His Hough transform research is multidisciplinary, incorporating perspectives in FLAME clustering, Clustering high-dimensional data, Correlation clustering, Single-linkage clustering and Algorithm.
His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Statistical learning, Embedding and Statistical relational learning and other disciplines.
His Pattern recognition study incorporates themes from Density based clustering, Entropy, Curse of dimensionality and Subspace clustering.
Peer Kröger performs multidisciplinary study on Data mining and Code in his works.
Between 2016 and 2021, his most popular works were:
- Dimensional testing for reverse k-nearest neighbor search (15 citations)
- Event-Enhanced Learning for KG Completion (11 citations)
- Palaeobiodiversity research based on stable isotopes: Correction of the sea spray effect on bone carbonate δ13C and δ18O by Gaussian Mixture Model clustering (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Database
His primary scientific interests are in Artificial intelligence, Data mining, Archaeology, δ18O and Knowledge graph.
His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
His Pattern recognition research incorporates themes from FLAME clustering and Correlation clustering, Clustering high-dimensional data, Cluster analysis, Fuzzy clustering.
His work carried out in the field of Data mining brings together such families of science as Point of interest, Structure, Simulation, Bayesian network and Global Positioning System.
His study on Animal bone and Prehistory is often connected to Human bone, Spatial heterogeneity and Georeference as part of broader study in Archaeology.
His studies deal with areas such as Embedding, Statistical learning, Missing data and Statistical relational learning as well as Knowledge graph.
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