D-Index & Metrics Best Publications

Jörg Sander

University of Alberta
Canada

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 42 Citations 47,837 87 World Ranking 5108 National Ranking 226

Research.com Recognitions

Awards & Achievements

2015 - ACM Distinguished Member

Overview

What is he best known for?

The fields of study he is best known for:

Machine learning
Statistics
Artificial intelligence

His primary areas of investigation include Data mining, DBSCAN, SUBCLU, OPTICS algorithm and Cluster analysis. His Data mining study combines topics in areas such as Quantization, Artificial intelligence, Outlier and Clustering high-dimensional data. His DBSCAN study introduces a deeper knowledge of CURE data clustering algorithm.

His research in CURE data clustering algorithm focuses on subjects like Algorithm, which are connected to Complete-linkage clustering. His study in Cluster analysis focuses on Correlation clustering and Single-linkage clustering. His research in Single-linkage clustering tackles topics such as FLAME clustering which are related to areas like Hierarchical clustering, Data set and Consensus clustering.

His most cited work include:

A density-based algorithm for discovering clusters in large spatial Databases with Noise (12123 citations)
LOF: identifying density-based local outliers (3662 citations)
OPTICS: ordering points to identify the clustering structure (2698 citations)

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

His primary scientific interests are in Data mining, Cluster analysis, Artificial intelligence, Pattern recognition and CURE data clustering algorithm. His studies deal with areas such as Spatial database, Outlier and Database as well as Data mining. His studies in Correlation clustering, Single-linkage clustering, Fuzzy clustering, Hierarchical clustering and DBSCAN are all subfields of Cluster analysis research.

His DBSCAN study which covers OPTICS algorithm that intersects with SUBCLU. In general Artificial intelligence study, his work on Voxel often relates to the realm of Density based, thereby connecting several areas of interest. His CURE data clustering algorithm study deals with Canopy clustering algorithm intersecting with Clustering high-dimensional data.

He most often published in these fields:

Data mining (60.98%)
Cluster analysis (46.34%)
Artificial intelligence (35.37%)

What were the highlights of his more recent work (between 2017-2020)?

Cluster analysis (46.34%)
Data mining (60.98%)
Density based clustering (6.10%)

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

Cluster analysis, Data mining, Density based clustering, Labeled data and Density based are his primary areas of study. His work blends Cluster analysis and Expectation–maximization algorithm studies together. His work investigates the relationship between Data mining and topics such as Hierarchical clustering that intersect with problems in Stability.

His Density based clustering study incorporates themes from Visualization, Computation, Theoretical computer science and Big data. Jörg Sander interconnects Contrast, Anomaly detection and Outlier in the investigation of issues within Labeled data. Jörg Sander integrates many fields, such as Density based, Semi supervised clustering, Graph, Pattern recognition and Artificial intelligence, in his works.

Between 2017 and 2020, his most popular works were:

Multi-Aspect Review-Team Assignment using Latent Research Areas (4 citations)
Internal Evaluation of Unsupervised Outlier Detection (3 citations)
A unified view of density-based methods for semi-supervised clustering and classification. (2 citations)

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)

23426 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)

7035 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)

5138 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)

1782 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)

1500 Citations

DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN

Erich Schubert;Jörg Sander;Martin Ester;Hans Peter Kriegel.
international conference on management of data (2017)

1076 Citations

Density-based clustering

Hans Peter Kriegel;Peer Kröger;Jörg Sander;Arthur Zimek.
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (2011)

864 Citations

Incremental Clustering for Mining in a Data Warehousing Environment

Martin Ester;Hans-Peter Kriegel;Jörg Sander;Michael Wimmer.
very large data bases (1998)

647 Citations

On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study

Guilherme O. Campos;Arthur Zimek;Jörg Sander;Ricardo J. Campello.
Data Mining and Knowledge Discovery (2016)

518 Citations

A distribution-based clustering algorithm for mining in large spatial databases

Xiaowei Xu;M. Ester;H.-P. Kriegel;J. Sander.
international conference on data engineering (1998)