H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 48 Citations 10,645 286 World Ranking 3200 National Ranking 142

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

MOA: Massive Online Analysis, a framework for stream classification and clustering.

Albert Bifet;Geoffrey Holmes;Bernhard Pfahringer;Philipp Kranen.
Proceedings of the First Workshop on Applications of Pattern Analysis (2010)

1691 Citations

3D Shape Histograms for Similarity Search and Classification in Spatial Databases

Mihael Ankerst;Gabi Kastenmüller;Hans-Peter Kriegel;Thomas Seidl.
Lecture Notes in Computer Science (1999)

821 Citations

Optimal multi-step k-nearest neighbor search

Thomas Seidl;Hans-Peter Kriegel.
international conference on management of data (1998)

653 Citations

Evaluating clustering in subspace projections of high dimensional data

Emmanuel Müller;Stephan Günnemann;Ira Assent;Thomas Seidl.
very large data bases (2009)

338 Citations

The ClusTree: indexing micro-clusters for anytime stream mining

Philipp Kranen;Ira Assent;Corinna Baldauf;Thomas Seidl.
Knowledge and Information Systems (2011)

274 Citations

Efficient User-Adaptable Similarity Search in Large Multimedia Databases

Thomas Seidl;Hans-Peter Kriegel.
very large data bases (1997)

247 Citations

Fast nearest neighbor search in high-dimensional space

S. Berchtold;B. Ertl;D.A. Keim;H.-P. Kriegel.
international conference on data engineering (1998)

228 Citations

Nearest Neighbor Classification in 3D Protein Databases

Mihael Ankerst;Gabi Kastenmüller;Hans-Peter Kriegel;Thomas Seidl.
intelligent systems in molecular biology (1999)

202 Citations

Managing Intervals Efficiently in Object-Relational Databases

Hans-Peter Kriegel;Marco Pötke;Thomas Seidl.
very large data bases (2000)

173 Citations

DUSC: Dimensionality Unbiased Subspace Clustering

I. Assent;R. Krieger;E. Muller;T. Seidl.
international conference on data mining (2007)

164 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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