J. Sander

发表

OPTICS: ordering points to identify the clustering structure

M. Ankerst, M. Breunig, H. Kriegel, 1999, ACM SIGMOD Conference.

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 1996, KDD.

LOF: identifying density-based local outliers

Hans-Peter Kriegel, Markus M. Breunig, Jörg Sander, 2000, SIGMOD 2000.

LOF: identifying density-based local outliers

Hans-Peter Kriegel, Markus M. Breunig, Jörg Sander, 2000, SIGMOD '00.

Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering

Jörg Sander, Gabriela Moise, J. Sander, 2008, KDD.

Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support

H. Kriegel, J. Sander, Martin Ester, 2000, Data Mining and Knowledge Discovery.

DBSCAN Revisited, Revisited

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 2017, ACM Trans. Database Syst..

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

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 1998, Data Mining and Knowledge Discovery.

Density-Based Clustering Based on Hierarchical Density Estimates

Ricardo J. G. B. Campello, Jörg Sander, Davoud Moulavi, 2013, PAKDD.

Knowledge Discovery in Databases

Martin Ester, Jörg Sander, J. Sander, 2000 .

A unified framework of density-based clustering for semi-supervised classification

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2018, SSDBM.

Effectively Retrieving Related Pages on the Web using Focused Co-citation

J. Sander, G. Moise, Davood Rafiei, 2005 .

Incremental and effective data summarization for dynamic hierarchical clustering

Corrine Cheng, Jörg Sander, Samer Nassar, 2004, SIGMOD '04.

- 1-Algorithms and Applications for Spatial Data Mining

H. Kriegel, J. Sander, Martin Ester, 2001 .

Knowledge Discovery in Spatial Databases

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 1999, DAGM-Symposium.

Proceedings of the 13th International Symposium on Advances in Spatial and Temporal Databases - Volume 8098

Hans-Peter Kriegel, Yu Zheng, Reynold Cheng, 2013 .

Analyzing and visualizing the canadian research landscape

Eleni Stroulia, Maryam Mirzaei, Victor do Nascimento Silva, 2019, Conference of the Centre for Advanced Studies on Collaborative Research.

Density-Based Clustering

J. Sander, 2017, Encyclopedia of Machine Learning and Data Mining.

Efficient Computation and Visualization of Multiple Density-Based Clustering Hierarchies

Ricardo J. G. B. Campello, Jörg Sander, Mario A. Nascimento, 2021, IEEE Transactions on Knowledge and Data Engineering.

A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies

A. Zimek, J. Sander, R. Campello, 2013, Data Mining and Knowledge Discovery.

Clustering for Mining in Large Spatial Databases

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 1998, Künstliche Intell..

Density‐based clustering

Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, 2011, WIREs Data Mining Knowl. Discov..

Automatic Extraction of Clusters from Hierarchical Clustering Representations

Zhiyong Lu, Xuejie Qin, Jörg Sander, 2003, PAKDD.

OPTICS: ordering points to identify the clustering structure

Hans-Peter Kriegel, Mihael Ankerst, Markus M. Breunig, 1999, SIGMOD '99.

Multi-Aspect Review-Team Assignment using Latent Research Areas

Eleni Stroulia, Jörg Sander, Maryam Mirzaei, 2019, Inf. Process. Manag..

Effective Summarization of Multi-Dimensional Data Streams for Historical Stream Mining

Jörg Sander, Samer Nassar, J. Sander, 2007, 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007).

A Modularity-Based Measure for Cluster Selection from Clustering Hierarchies

Ricardo J. G. B. Campello, Jörg Sander, Jadson Castro Gertrudes, 2018, AusDM.

Hierarchical Density-Based Clustering Using MapReduce

Ricardo J. G. B. Campello, Jörg Sander, Murilo Coelho Naldi, 2019, IEEE Transactions on Big Data.

Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2015, ACM Trans. Knowl. Discov. Data.

Density-Based Clustering Validation

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2014, SDM.

Model Selection for Semi-Supervised Clustering

Arthur Zimek, Randy Goebel, Ricardo J. G. B. Campello, 2014, EDBT.

Finding Surprisingly Frequent Patterns of Variable Lengths in Sequence Data

Jörg Sander, Davood Rafiei, Reza Sadoddin, 2016, SDM.

A unified view of density-based methods for semi-supervised clustering and classification

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2019, Data Mining and Knowledge Discovery.

cisRED: a database system for genome-scale computational discovery of regulatory elements

Obi L. Griffith, X. Zhang, Steven J. M. Jones, 2005, Nucleic Acids Res..

Ensembles for unsupervised outlier detection: challenges and research questions a position paper

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2014, SKDD.

Semi-supervised Density-Based Clustering

Jörg Sander, Levi Lelis, Levi H. S. Lelis, 2009, 2009 Ninth IEEE International Conference on Data Mining.

Combining gene expression and interaction network data to improve kidney lesion score prediction

Russell Greiner, Jörg Sander, Mohsen Hajiloo, 2012, Int. J. Bioinform. Res. Appl..

Generalized Density-Based Clustering for Spatial Data Mining (Abstract)

Jörg Sander, J. Sander, 1999, Datenbank Rundbr..

A Classification-Based Glioma Diffusion Model Using MRI Data

Mark W. Schmidt, Russell Greiner, Jörg Sander, 2006, Canadian Conference on AI.

Learning a Classification-based Glioma Growth Model Using MRI Data

Mark W. Schmidt, Russell Greiner, Jörg Sander, 2006, J. Comput..

On the Evaluation of Outlier Detection and One-Class Classification Methods

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

On the internal evaluation of unsupervised outlier detection

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2015, SSDBM.

Data perturbation for outlier detection ensembles

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2014, SSDBM '14.

Subsampling for efficient and effective unsupervised outlier detection ensembles

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2013, KDD.

OPTICS-OF: Identifying Local Outliers

Hans-Peter Kriegel, Markus M. Breunig, Jörg Sander, 1999, PKDD.

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

Michael E. Houle, A. Zimek, J. Sander, 2016, Data Mining and Knowledge Discovery.

Internal Evaluation of Unsupervised Outlier Detection

Arthur Zimek, Ricardo J. G. B. Campello, Henrique O. Marques, 2020, ACM Trans. Knowl. Discov. Data.

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

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 1998, Proceedings 14th International Conference on Data Engineering.

INTEGRATED CLUSTERING FOR MICROARRAY DATA

Jörg Sander, Gabriela Moise, J. Sander, 2006 .

ONDOCS: Ordering Nodes to Detect Overlapping Community Structure

Randy Goebel, Osmar R. Zaïane, Jörg Sander, 2010, Data Mining for Social Network Data.

Analysis of SIGMOD's co-authorship graph

Jörg Sander, Mario A. Nascimento, Jeffrey Pound, 2003, SGMD.

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 2001, IEEE Trans. Knowl. Data Eng..

Efficiently supporting multiple similarity queries for mining in metric databases

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

Knowledge Discovery in Spatial Databases

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 1999, KI.

Database Primitives for Spatial Data Mining

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 1999, BTW.

Clustering and knowledge discovery in spatial databases

Hans-Peter Kriegel, Xiaowei Xu, Martin Ester, 1997 .

Algorithms and Applications for Spatial Data Mining

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 2001 .

Incremental Clustering for Mining in a Data Warehousing Environment

Hans-Peter Kriegel, Michael Wimmer, Martin Ester, 1998, VLDB.

Ordering The Database With Respect To The Clustering Structure 3

M. Ankerst, M. Breunig, H. Kriegel, 1999 .

Mining Statistically Significant Co-location and Segregation Patterns

Jörg Sander, Sajib Barua, J. Sander, 2014, IEEE Transactions on Knowledge and Data Engineering.

On strategies for building effective ensembles of relative clustering validity criteria

Pablo A. Jaskowiak, A. Zimek, J. Sander, 2016, Knowledge and Information Systems.

Efficient Indexing of High Dimensional Normalized Histograms

Jörg Sander, Mario A. Nascimento, Alexandru Coman, 2003, DEXA.

Fast Hierarchical Clustering Based on Compressed Data and OPTICS

Hans-Peter Kriegel, Markus M. Breunig, Jörg Sander, 2000, PKDD.

Independent quantization: an index compression technique for high-dimensional data spaces

Christian Böhm, Hans-Peter Kriegel, Stefan Berchtold, 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

Decomposing object-oriented class modules using an agglomerative clustering technique

Jörg Sander, Alexander Chatzigeorgiou, Nikolaos Tsantalis, 2009, 2009 IEEE International Conference on Software Maintenance.

Exploiting redundancy in sensor networks for energy efficient processing of spatiotemporal region queries

Jörg Sander, Mario A. Nascimento, Alexandru Coman, 2005, CIKM '05.

Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines

Mark W. Schmidt, Chi-Hoon Lee, Russell Greiner, 2005, CVBIA.

Subspace and projected clustering: experimental evaluation and analysis

Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, 2009, Knowledge and Information Systems.

Under Consideration for Publication in Knowledge and Information Systems Robust Projected Clustering

J. Sander, G. Moise, J. Sander, 2022 .

Adaptive processing of historical spatial range queries in peer-to-peer sensor networks

Jörg Sander, Mario A. Nascimento, Alexandru Coman, 2007, Distributed and Parallel Databases.

Active Learning Strategies for Semi-Supervised DBSCAN

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2014, Canadian Conference on AI.

Density-Connected Sets and their Application for Trend Detection in Spatial Databases

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 1997, KDD.

Efficient composite pattern finding from monad patterns

Jörg Sander, Guohui Lin, Jianjun Zhou, 2007, Int. J. Bioinform. Res. Appl..

Ieee Transactions on Knowledge and Data Engineering 1 Effectively Retrieving Related Pages on the Web Using Focused Co-citation

J. Sander, G. Moise, Davood Rafiei, 2004 .

On Join Location in Sensor Networks

Jörg Sander, Mario A. Nascimento, Alexandru Coman, 2007, 2007 International Conference on Mobile Data Management.

Mining statistically sound co-location patterns at multiple distances

Jörg Sander, Sajib Barua, J. Sander, 2014, SSDBM '14.

Density-Based Clustering

J. Sander, 2017, Encyclopedia of Machine Learning and Data Mining.

MustaCHE: A Multiple Clustering Hierarchies Explorer

Ricardo J. G. B. Campello, Jörg Sander, Antônio C. Araújo Neto, 2018, Proc. VLDB Endow..

Efficient Computation of Multiple Density-Based Clustering Hierarchies

Ricardo J. G. B. Campello, Jörg Sander, Antônio C. Araújo Neto, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data

J. Sander, R. Campello, D. Moulavi, 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Speedup Clustering with Hierarchical Ranking

Jörg Sander, Jianjun Zhou, J. Sander, 2006, Sixth International Conference on Data Mining (ICDM'06).

Data Bubbles for Non-Vector Data: Speeding-up Hierarchical Clustering in Arbitrary Metric Spaces

Jörg Sander, Jianjun Zhou, J. Sander, 2003, VLDB.

Hierarchical cluster analysis of SAGE data for cancer profiling

Jörg Sander, Raymond T. Ng, Monica C. Sleumer, 2001, BIOKDD.

Data bubbles: quality preserving performance boosting for hierarchical clustering

Hans-Peter Kriegel, Peer Kröger, Markus M. Breunig, 2001, SIGMOD '01.

A methodology for analyzing SAGE libraries for cancer profiling

Steven J. M. Jones, Jörg Sander, Raymond T. Ng, 2005, TOIS.

A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2013, Data Mining and Knowledge Discovery.

Focused Co-citation: Improving the Retrieval of Related Pages on the Web

Jörg Sander, Gabriela Moise, Davood Rafiei, 2003, WWW.