H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 97 Citations 83,267 373 World Ranking 173 National Ranking 10

Research.com Recognitions

Awards & Achievements

2009 - ACM Fellow For contributions to knowledge discovery and data mining, similarity search, spatial data management, and access methods for high-dimensional data.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Database

Hans-Peter Kriegel mainly focuses on Data mining, Artificial intelligence, Cluster analysis, Pattern recognition and Database. His Data mining research includes elements of Algorithm, Theoretical computer science and Graph. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Linear subspace.

All of his Cluster analysis and CURE data clustering algorithm, Correlation clustering, Fuzzy clustering, DBSCAN and Single-linkage clustering investigations are sub-components of the entire Cluster analysis study. His DBSCAN research incorporates elements of OPTICS algorithm, SUBCLU and k-medians clustering. His work carried out in the field of Database brings together such families of science as Spatial database, Nearest neighbor search, Data visualization and Knowledge acquisition.

His most cited work include:

  • A density-based algorithm for discovering clusters in large spatial Databases with Noise (12123 citations)
  • The R*-tree: an efficient and robust access method for points and rectangles (3895 citations)
  • LOF: identifying density-based local outliers (3662 citations)

What are the main themes of his work throughout his whole career to date?

Hans-Peter Kriegel mostly deals with Data mining, Artificial intelligence, Cluster analysis, Nearest neighbor search and Theoretical computer science. The study incorporates disciplines such as Spatial query, Spatial database, Algorithm and Database in addition to Data mining. The Database study combines topics in areas such as Probabilistic logic and Information retrieval.

His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Computer vision and Pattern recognition. His work in CURE data clustering algorithm, Correlation clustering, Fuzzy clustering, Clustering high-dimensional data and Data stream clustering is related to Cluster analysis. His research integrates issues of OPTICS algorithm and SUBCLU in his study of DBSCAN.

He most often published in these fields:

  • Data mining (56.76%)
  • Artificial intelligence (24.55%)
  • Cluster analysis (22.52%)

What were the highlights of his more recent work (between 2009-2020)?

  • Data mining (56.76%)
  • Artificial intelligence (24.55%)
  • Pattern recognition (13.74%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Data mining, Artificial intelligence, Pattern recognition, Cluster analysis and Probabilistic logic. His biological study spans a wide range of topics, including Theoretical computer science, Clustering high-dimensional data and k-nearest neighbors algorithm. His Artificial intelligence research includes elements of Machine learning, Linear subspace and Computer vision.

In general Cluster analysis study, his work on Fuzzy clustering often relates to the realm of Density based, thereby connecting several areas of interest. His studies in Probabilistic logic integrate themes in fields like Probability distribution, Database, Time complexity, Probabilistic database and Uncertain data. His Constrained clustering study which covers Data stream clustering that intersects with Determining the number of clusters in a data set.

Between 2009 and 2020, his most popular works were:

  • A Three-Way Model for Collective Learning on Multi-Relational Data (1007 citations)
  • DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN (423 citations)
  • Density‐based clustering (417 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

Data mining, Artificial intelligence, Anomaly detection, Cluster analysis and Pattern recognition are his primary areas of study. His studies deal with areas such as Probabilistic logic, Theoretical computer science, Clustering high-dimensional data and Search engine indexing as well as Data mining. The various areas that Hans-Peter Kriegel examines in his Probabilistic logic study include Probabilistic database, Nearest neighbor search and Database.

His research in Artificial intelligence intersects with topics in Data type, Machine learning and Computer vision. The Anomaly detection study combines topics in areas such as Algorithm, Scalability and Outlier. Hans-Peter Kriegel interconnects Recommender system and Aggregate in the investigation of issues within Cluster analysis.

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.

Best Publications

A density-based algorithm for discovering clusters in large spatial Databases with Noise

Martin Ester;Hans-Peter Kriegel;Jörg Sander;Xiaowei Xu.
knowledge discovery and data mining (1996)

18915 Citations

The R*-tree: an efficient and robust access method for points and rectangles

Norbert Beckmann;Hans-Peter Kriegel;Ralf Schneider;Bernhard Seeger.
international conference on management of data (1990)

6422 Citations

LOF: identifying density-based local outliers

Markus M. Breunig;Hans-Peter Kriegel;Raymond T. Ng;Jörg Sander.
international conference on management of data (2000)

5344 Citations

OPTICS: ordering points to identify the clustering structure

Mihael Ankerst;Markus M. Breunig;Hans-Peter Kriegel;Jörg Sander.
international conference on management of data (1999)

4213 Citations

The X-tree: an index structure for high-dimensional data

Stefan Berchtold;Daniel A. Keim;Hans-Peter Kriegel.
very large data bases (2001)

2204 Citations

Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications

Jörg Sander;Martin Ester;Hans-Peter Kriegel;Xiaowei Xu.
Data Mining and Knowledge Discovery (1998)

1490 Citations

A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise

Martin Ester;Hans-Peter Kriegel;Jörg Sander;Xiaowei Xu.
knowledge discovery and data mining (1996)

1317 Citations

Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering

Hans-Peter Kriegel;Peer Kröger;Arthur Zimek.
ACM Transactions on Knowledge Discovery From Data (2009)

1187 Citations

A Three-Way Model for Collective Learning on Multi-Relational Data

Maximilian Nickel;Volker Tresp;Hans-peter Kriegel.
international conference on machine learning (2011)

1049 Citations

Efficient processing of spatial joins using R-trees

Thomas Brinkhoff;Hans-Peter Kriegel;Bernhard Seeger.
international conference on management of data (1993)

959 Citations

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