What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
The scientist’s investigation covers issues in Data mining, Cluster analysis, Artificial intelligence, Pattern recognition and Nearest neighbor search.
His Data mining study incorporates themes from Linear subspace, Database, Constrained clustering, Dimensionality reduction and Search engine indexing.
His studies deal with areas such as Subspace topology and Data stream mining as well as Cluster analysis.
As a part of the same scientific family, Thomas Seidl mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Field and Data set.
Thomas Seidl combines subjects such as Ranking, Histogram, Quadratic form and Categorical variable with his study of Pattern recognition.
The study incorporates disciplines such as Similarity, Similitude, Filter and Series in addition to Nearest neighbor search.
His most cited work include:
- 3D Shape Histograms for Similarity Search and Classification in Spatial Databases (498 citations)
- Optimal multi-step k-nearest neighbor search (431 citations)
- Evaluating clustering in subspace projections of high dimensional data (190 citations)
What are the main themes of his work throughout his whole career to date?
Data mining, Artificial intelligence, Cluster analysis, Pattern recognition and Nearest neighbor search are his primary areas of study.
His research investigates the connection between Data mining and topics such as Database that intersect with problems in Information retrieval.
His Artificial intelligence research integrates issues from Machine learning and Computer vision.
His works in Correlation clustering, CURE data clustering algorithm, Clustering high-dimensional data, Data stream clustering and Fuzzy clustering are all subjects of inquiry into Cluster analysis.
His Pattern recognition research includes themes of Curse of dimensionality and Image retrieval.
His biological study spans a wide range of topics, including Filter, Earth mover's distance, Similarity, Algorithm and Similitude.
He most often published in these fields:
- Data mining (50.89%)
- Artificial intelligence (34.43%)
- Cluster analysis (31.65%)
What were the highlights of his more recent work (between 2017-2021)?
- Cluster analysis (31.65%)
- Artificial intelligence (34.43%)
- Data mining (50.89%)
In recent papers he was focusing on the following fields of study:
Thomas Seidl mainly investigates Cluster analysis, Artificial intelligence, Data mining, Pattern recognition and Algorithm.
Thomas Seidl interconnects Subspace topology, Point and Measure in the investigation of issues within Cluster analysis.
His work deals with themes such as Correlation clustering and Linear subspace, which intersect with Subspace topology.
The Artificial intelligence study combines topics in areas such as Machine learning and Scalability.
Thomas Seidl has included themes like Ground truth and Unsupervised learning in his Data mining study.
His study in the field of Principal component analysis also crosses realms of Codec.
Between 2017 and 2021, his most popular works were:
- BFSPMiner : an effective and efficient batch-free algorithm for mining sequential patterns over data streams (9 citations)
- TACAM: Topic And Context Aware Argument Mining (6 citations)
- Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned (4 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary scientific interests are in Artificial intelligence, Cluster analysis, Knowledge graph, Data mining and Pattern recognition.
His Ranking and Visualization study in the realm of Artificial intelligence connects with subjects such as Event, Trace and Anomaly.
His work on DBSCAN as part of general Cluster analysis research is often related to Clockwork, thus linking different fields of science.
While the research belongs to areas of Knowledge graph, Thomas Seidl spends his time largely on the problem of Machine learning, intersecting his research to questions surrounding Graph.
His Data mining research includes elements of Database transaction, Associative property and Pruning.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Matrix and Outlier.
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