Arthur Zimek

发表

Evaluation of Clusterings -- Metrics and Visual Support

Elke Achtert, Hans-Peter Kriegel, Arthur Zimek, 2012, 2012 IEEE 28th International Conference on Data Engineering.

Absolute Cluster Validity

Arthur Zimek, Tanja Zseby, Felix Iglesias, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Density-Based Clustering Validation

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

Subspace clustering

Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, 2012, WIREs Data Mining Knowl. Discov..

A survey on unsupervised outlier detection in high‐dimensional numerical data

Hans-Peter Kriegel, Arthur Zimek, Erich Schubert, 2012, Stat. Anal. Data Min..

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.

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

Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, 2009, TKDD.

Angle-based outlier detection in high-dimensional data

Hans-Peter Kriegel, Arthur Zimek, Matthias Schubert, 2008, KDD.

LoOP: local outlier probabilities

Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, 2009, CIKM.

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.

Interpreting and Unifying Outlier Scores

Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, 2011, SDM.

Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?

A. Zimek, H. Kriegel, Erich Schubert, 2010, SSDBM.

ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg" pdf

Arthur Zimek, Erich Schubert, A. Zimek, 2019, ArXiv.

There and back again: Outlier detection between statistical reasoning and data mining algorithms

Arthur Zimek, Peter Filzmoser, A. Zimek, 2018, WIREs Data Mining Knowl. Discov..

Density‐based clustering

R. Campello, Peer Kröger, Jörg Sander, 2019, WIREs Data Mining Knowl. Discov..

Improving Semantic Similarity of Words by Retrofitting Word Vectors in Sense Level

Arthur Zimek, Rui Zhang, Peter Schneider-Kamp, 2020, ICAART.

The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives

Arthur Zimek, Jilles Vreeken, A. Zimek, 2013, Machine Learning.

An Unsupervised Boosting Strategy for Outlier Detection Ensembles

Arthur Zimek, Wagner Meira, Guilherme Oliveira Campos, 2018, PAKDD.

Data perturbation for outlier detection ensembles

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

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

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

Subsampling for efficient and effective unsupervised outlier detection ensembles

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

On Evaluation of Outlier Rankings and Outlier Scores

Hans-Peter Kriegel, Arthur Zimek, Erich Schubert, 2012, SDM.

On the Correlation Between Local Intrinsic Dimensionality and Outlierness

Arthur Zimek, Michael E. Houle, Erich Schubert, 2018, SISAP.

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.

Outlier Detection in Urban Traffic Data

Arthur Zimek, Youcef Djenouri, A. Zimek, 2018, WIMS.

Are Network Attacks Outliers? A Study of Space Representations and Unsupervised Algorithms

Arthur Zimek, Tanja Zseby, Félix Iglesias, 2019, PKDD/ECML Workshops.

Density‐based clustering

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

Clustering refinement

Arthur Zimek, Tanja Zseby, Félix Iglesias, 2021, International Journal of Data Science and Analytics.

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

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

Active Learning Strategies for Semi-Supervised DBSCAN

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

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.

Evaluation of Multiple Clustering Solutions

Hans-Peter Kriegel, Arthur Zimek, Erich Schubert, 2011, MultiClust@ECML/PKDD.

Density Based Subspace Clustering over Dynamic Data

Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, 2011, SSDBM.

Towards subspace clustering on dynamic data: an incremental version of PreDeCon

A. Zimek, H. Kriegel, Eirini Ntoutsi, 2010, StreamKDD '10.

Correlation clustering

Arthur Zimek, A. Zimek, Arthur Zimek, 2009, SKDD.

Subspace and projected clustering: experimental evaluation and analysis

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

A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms

Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, 2008, SSDBM.

ELKI: A Software System for Evaluation of Subspace Clustering Algorithms

Elke Achtert, Hans-Peter Kriegel, Arthur Zimek, 2008, SSDBM.

On Exploring Complex Relationships of Correlation Clusters

Elke Achtert, Christian Böhm, Hans-Peter Kriegel, 2007, 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007).

Detection and Visualization of Subspace Cluster Hierarchies

Elke Achtert, Hans-Peter Kriegel, Arthur Zimek, 2007, DASFAA.

COSMIC: Conceptually Specified Multi-Instance Clusters

Hans-Peter Kriegel, Arthur Zimek, Matthias Schubert, 2006, Sixth International Conference on Data Mining (ICDM'06).

Finding Hierarchies of Subspace Clusters

Elke Achtert, Christian Böhm, Hans-Peter Kriegel, 2006, PKDD.

Mining Hierarchies of Correlation Clusters

Elke Achtert, Christian Böhm, Peer Kröger, 2006, 18th International Conference on Scientific and Statistical Database Management (SSDBM'06).

Computing Clusters of Correlation Connected objects

Christian Böhm, Peer Kröger, Arthur Zimek, 2004, SIGMOD '04.

Arthur Zimek, Björn Þór Jónsson, Ilaria Bartolini, 2020, Lecture Notes in Computer Science.

Generalized Outlier Detection with Flexible Kernel Density Estimates

Hans-Peter Kriegel, Arthur Zimek, Erich Schubert, 2014, SDM.

Frequent Pattern Mining Algorithms for Data Clustering

Arthur Zimek, Ira Assent, Jilles Vreeken, 2014, Frequent Pattern Mining.

Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection

A. Zimek, H. Kriegel, Erich Schubert, 2012, Data Mining and Knowledge Discovery.

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Platinum Sponsor Gold Sponsor Technical Support Silver Sponsors Bronze Sponsors Organizing Institutions Marathon Data Systems 2nd Multiclust Workshop: Discovering, Summarizing and Using Multiple C

Michael E. Houle, Wohlgemuth, Kin Man Poon, 2011 .

On Using Class-Labels in Evaluation of Clusterings

A. Zimek, H. Kriegel, Stephan Günnemann, 2010 .

Density-Based Data Analysis and Similarity Search

Hans-Peter Kriegel, Arthur Zimek, Martin Pfeifle, 2007 .

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.

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 evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles

Henrique O. Marques, A. Zimek, Jörg Sander, 2023, Data Mining and Knowledge Discovery.

Future trends in data mining

Hans-Peter Kriegel, Peer Kröger, Arthur Zimek, 2007, Data Mining and Knowledge Discovery.

Discriminative features for identifying and interpreting outliers

Arthur Zimek, Ira Assent, Raymond T. Ng, 2014, 2014 IEEE 30th International Conference on 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.