Jörg Sander

发表

OPTICS: ordering points to identify the clustering structure

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

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).

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

Jörg Sander, Jianjun Zhou, 2003, VLDB.

Data perturbation for outlier detection ensembles

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

Incremental Clustering for Mining in a Data Warehousing Environment

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

Density-Based Clustering

Jörg Sander, 2010, Encyclopedia of Machine Learning.

Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases

Reynold Cheng, Jörg Sander, Mario A. Nascimento, 2011 .

Semi-supervised Density-Based Clustering

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

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.

MustaCHE: A Multiple Clustering Hierarchies Explorer

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

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

Arthur Zimek, Ira Assent, Ricardo J. G. B. Campello, 2016, Data Mining and Knowledge Discovery.

Decomposing object-oriented class modules using an agglomerative clustering technique

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

Finding Surprisingly Frequent Patterns of Variable Lengths in Sequence Data

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

Data bubbles: quality preserving performance boosting for hierarchical clustering

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

Discovering Spatial Co-Clustering Patterns in Traffic Collision Data

Dapeng Li, Jörg Sander, Mario A. Nascimento, 2013, IWCTS '13.

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

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

LOF: identifying density-based local outliers

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

A framework for spatio-temporal query processing over wireless sensor networks

Jörg Sander, Mario A. Nascimento, Alexandru Coman, 2004, DMSN '04.

Mining statistically sound co-location patterns at multiple distances

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

Subspace and projected clustering: experimental evaluation and analysis

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

On the internal evaluation of unsupervised outlier detection

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

Spatial Data Mining: A Database Approach

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

Learning a Classification-based Glioma Growth Model Using MRI Data

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

A Classification-Based Glioma Diffusion Model Using MRI Data

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

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.

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

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

Density-Based Clustering Validation

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

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).

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

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

A Trajectory Splitting Model for Efficient Spatio-Temporal Indexing

Jörg Sander, Mario A. Nascimento, James Elding, 2005, VLDB.

Density‐based clustering

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

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).

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.

Density-Based Clustering Based on Hierarchical Density Estimates

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

DBSCAN Revisited, Revisited

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

Knowledge Discovery in Spatial Databases

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

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.

An Analysis of Spatio-Temporal Query Processing in Sensor Networks

Jörg Sander, Mario A. Nascimento, Alexandru Coman, 2005, 21st International Conference on Data Engineering Workshops (ICDEW'05).

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).

Automatic Extraction of Clusters from Hierarchical Clustering Representations

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

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

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.

Database Primitives for Spatial Data Mining

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

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).

Density‐based clustering

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2020, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

Active Semi-Supervised Classification Based on Multiple Clustering Hierarchies

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

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

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

Heavyweight Pattern Mining in Attributed Flow Graphs

José Nelson Amaral, Jörg Sander, Li Ding, 2014, 2014 IEEE International Conference on Data Mining.

LOF: identifying density-based local outliers

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

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.

PIST: An Efficient and Practical Indexing Technique for Historical Spatio-Temporal Point Data

Jörg Sander, Mario A. Nascimento, Daniel Mallett, 2008, GeoInformatica.

Clustering for Mining in Large Spatial Databases

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

Active Learning Strategies for Semi-Supervised DBSCAN

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

Analysis of SIGMOD's co-authorship graph

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

On Join Location in Sensor Networks

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

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..

Multi-Aspect Review-Team Assignment using Latent Research Areas

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

Clustering and knowledge discovery in spatial databases

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

INTEGRATED CLUSTERING FOR MICROARRAY DATA

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

Knowledge Discovery in Spatial Databases

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

OPTICS-OF: Identifying Local Outliers

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

A methodology for analyzing SAGE libraries for cancer profiling

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

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

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

Detecting Statistically Significant Temporal Associations from Multiple Event Sequences

Jörg Sander, Han Liang, 2013, Canadian Conference on AI.

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

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2020, Data Mining and Knowledge Discovery.

Efficient Indexing of High Dimensional Normalized Histograms

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

Algorithms for Characterization and Trend Detection in Spatial Databases

Hans-Peter Kriegel, Martin Ester, Jörg Sander, 1998, KDD.

On strategies for building effective ensembles of relative clustering validity criteria

Arthur Zimek, Ricardo J. G. B. Campello, Jörg Sander, 2015, Knowledge and Information Systems.

Towards the qualitative, plan-based simulation of international crises

Jörg Sander, Wolfgang Balzer, W. Balzer, 1999, Eur. J. Oper. Res..

Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines

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

Mutual information for unsupervised deep learning image registration

Jörg Sander, Marius Staring, Bas H. M. van der Velden, 2020, Medical Imaging: Image Processing.

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.

SSCP: Mining Statistically Significant Co-location Patterns

Jörg Sander, Sajib Barua, 2011, SSTD.

Fast Hierarchical Clustering Based on Compressed Data and OPTICS

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

Statistically Significant Co-location Pattern Mining

Jörg Sander, Sajib Barua, 2017, Encyclopedia of GIS.

Hierarchical Density-Based Clustering Using MapReduce

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

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..

Knowledge Discovery in Databases - Techniken und Anwendungen

Martin Ester, Jörg Sander, Jörg Sander, 2000 .

Robust projected clustering

Martin Ester, Jörg Sander, Gabriela Moise, 2007, Knowledge and Information Systems.

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.

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

Ricardo J. G. B. Campello, Jörg Sander, Davoud Moulavi, 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Speedup Clustering with Hierarchical Ranking

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

Finding the Nearest Neighbors in Biological Databases Using Less Distance Computations

Lusheng Wang, Jörg Sander, Guohui Lin, 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Subsampling for efficient and effective unsupervised outlier detection ensembles

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

Model Selection for Semi-Supervised Clustering

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

Hierarchical cluster analysis of SAGE data for cancer profiling

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

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

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

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..

Algorithms and Applications for Spatial Data Mining

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

Mining Statistically Significant Co-location and Segregation Patterns

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

A Modularity-Based Measure for Cluster Selection from Clustering Hierarchies

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

ONDOCS: Ordering Nodes to Detect Overlapping Community Structure

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

Incremental and effective data summarization for dynamic hierarchical clustering

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

Density-Based Clustering

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

Knowledge Discovery in Databases

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