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Arthur Zimek
Person information
- affiliation: University of Southern Denmark, Odense, Denmark
- affiliation: Ludwig Maximilian University of Munich, Germany
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2020 – today
- 2024
- [c78]Alastair Anderberg, James Bailey, Ricardo J. G. B. Campello, Michael E. Houle, Henrique O. Marques
, Milos Radovanovic, Arthur Zimek
Dimensionality-Aware Outlier Detection. SDM 2024: 652-660 - [c77]Muhammad Rajabinasab, Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek:
A Dynamic Evaluation Metric for Feature Selection. SISAP 2024: 65-72 - [c76]Camilla Birch Okkels, Martin Aumüller, Arthur Zimek:
On the Design of Scalable Outlier Detection Methods Using Approximate Nearest Neighbor Graphs. SISAP 2024: 170-184 - [c75]Philipp Röchner, Henrique O. Marques, Ricardo J. G. B. Campello, Arthur Zimek, Franz Rothlauf:
Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers. SISAP 2024: 215-222 - [i13]Alastair Anderberg, James Bailey, Ricardo J. G. B. Campello, Michael E. Houle, Henrique O. Marques, Milos Radovanovic, Arthur Zimek:
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis. CoRR abs/2401.05453 (2024) - [i12]Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek, Peter Schneider-Kamp:
SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data. CoRR abs/2404.15821 (2024) - [i11]Muhammad Rajabinasab, Anton Danholt Lautrup, Tobias Hyrup, Arthur Zimek:
FSDEM: Feature Selection Dynamic Evaluation Metric. CoRR abs/2408.14234 (2024) - [i10]Philipp Röchner, Henrique O. Marques, Ricardo J. G. B. Campello, Arthur Zimek, Franz Rothlauf:
Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers (Extended Version). CoRR abs/2408.15874 (2024) - 2023
- [j38]Henrique O. Marques
On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles. Data Min. Knowl. Discov. 37(4): 1473-1517 (2023) - [j37]Félix Iglesias Vázquez
Anomaly detection in streaming data: A comparison and evaluation study. Expert Syst. Appl. 233: 120994 (2023) - [c74]Jonatan Møller Nuutinen Gøttcke, Colin Bellinger, Paula Branco, Arthur Zimek
An Interpretable Measure of Dataset Complexity for Imbalanced Classification Problems. SDM 2023: 253-261 - [c73]Félix Iglesias
SDOclust: Clustering with Sparse Data Observers. SISAP 2023: 185-199 - [i9]Yi Cai, Arthur Zimek
Explaining text classifiers through progressive neighborhood approximation with realistic samples. CoRR abs/2302.07733 (2023) - [i8]Tobias Hyrup, Anton Danholt Lautrup, Arthur Zimek, Peter Schneider-Kamp:
Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data. CoRR abs/2312.12216 (2023) - 2022
- [c72]Rui Zhang
A Simple Meta-path-free Framework for Heterogeneous Network Embedding. CIKM 2022: 2600-2609 - [c71]Rui Zhang
Unsupervised Representation Learning on Attributed Multiplex Network. CIKM 2022: 2610-2619 - [c70]Yi Cai
Power of Explanations: Towards automatic debiasing in hate speech detection. DSAA 2022: 1-10 - [c69]Erik Andersen
Evaluation of Probability Distribution Distance Metrics in Traffic Flow Outlier Detection. MDM 2022: 64-69 - [c68]Conor McCarthy
Analyzing Passing Sequences for the Prediction of Goal-Scoring Opportunities. MLSA@PKDD/ECML 2022: 27-40 - [c67]Henrique O. Marques
Similarity-Based Unsupervised Evaluation of Outlier Detection. SISAP 2022: 234-248 - [i7]Yi Cai, Arthur Zimek
Power of Explanations: Towards automatic debiasing in hate speech detection. CoRR abs/2209.09975 (2022) - 2021
- [j36]Félix Iglesias
Clustering refinement. Int. J. Data Sci. Anal. 12(4): 333-353 (2021) - [c66]Yi Cai
XPROAX-Local explanations for text classification with progressive neighborhood approximation. DSAA 2021: 1-10 - [c65]Nicklas Sindlev Andersen, Marco Chiarandini, Stefan Jänicke, Panagiotis Tampakis
Detecting Wandering Behavior of People with Dementia. ICDM (Workshops) 2021: 727-733 - [c64]Jonatan Møller Nuutinen Gøttcke
Non-parametric Semi-supervised Learning by Bayesian Label Distribution Propagation. SISAP 2021: 118-132 - [c63]Jonatan Møller Nuutinen Gøttcke
Handling Class Imbalance in k-Nearest Neighbor Classification by Balancing Prior Probabilities. SISAP 2021: 247-261 - [i6]Yi Cai, Arthur Zimek, Eirini Ntoutsi:
XPROAX-Local explanations for text classification with progressive neighborhood approximation. CoRR abs/2109.15004 (2021) - [i5]Nicklas Sindlev Andersen, Marco Chiarandini, Stefan Jänicke, Panagiotis Tampakis, Arthur Zimek:
Detecting Wandering Behavior of People with Dementia. CoRR abs/2110.13128 (2021) - 2020
- [j35]Eirini Ntoutsi, Erich Schubert, Arthur Zimek
Call for Special Issue Papers: Evaluation and Experimental Design in Data Mining and Machine Learning. Big Data 8(4): 253-254 (2020) - [j34]Eirini Ntoutsi, Erich Schubert, Arthur Zimek
Call for Special Issue Papers: Evaluation and Experimental Design in Data Mining and Machine Learning. Big Data 8(5): 456-457 (2020) - [j33]Eirini Ntoutsi, Erich Schubert, Arthur Zimek
Call for Special Issue Papers: Evaluation and Experimental Design in Data Mining and Machine Learning. Big Data 8(6): 546-547 (2020) - [j32]Jadson Castro Gertrudes
Correction to: A unified view of density-based methods for semi-supervised clustering and classification. Data Min. Knowl. Discov. 34(6): 1984-1985 (2020) - [j31]Félix Iglesias
Absolute Cluster Validity. IEEE Trans. Pattern Anal. Mach. Intell. 42(9): 2096-2112 (2020) - [j30]Henrique O. Marques
Internal Evaluation of Unsupervised Outlier Detection. ACM Trans. Knowl. Discov. Data 14(4): 47:1-47:42 (2020) - [j29]Ricardo J. G. B. Campello, Peer Kröger, Jörg Sander, Arthur Zimek
Density-based clustering. WIREs Data Mining Knowl. Discov. 10(2) (2020) - [c62]Félix Iglesias Vázquez
Interpretability and Refinement of Clustering. DSAA 2020: 21-29 - [c61]Rui Zhang
Matching Research Publications to the United Nations' Sustainable Development Goals by Multi-Label-Learning with Hierarchical Categories. DSAA 2020: 516-525 - [c60]Rui Zhang
Improving Semantic Similarity of Words by Retrofitting Word Vectors in Sense Level. ICAART (2) 2020: 108-119 - [c59]Jonas Herskind Sejr, Arthur Zimek
Explainable Detection of Zero Day Web Attacks. ICDIS 2020: 71-78 - [e5]Irena Koprinska
ECML PKDD 2020 Workshops - Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14-18, 2020, Proceedings. Communications in Computer and Information Science 1323, Springer 2020, ISBN 978-3-030-65964-6 [contents] - [e4]Shin'ichi Satoh, Lucia Vadicamo
Similarity Search and Applications - 13th International Conference, SISAP 2020, Copenhagen, Denmark, September 30 - October 2, 2020, Proceedings. Lecture Notes in Computer Science 12440, Springer 2020, ISBN 978-3-030-60935-1 [contents]
2010 – 2019
- 2019
- [j28]Félix Iglesias
MDCGen: Multidimensional Dataset Generator for Clustering. J. Classif. 36(3): 599-618 (2019) - [j27]Guilherme Oliveira Campos, Edré Moreira, Wagner Meira Jr., Arthur Zimek
Outlier detection in graphs: A study on the impact of multiple graph models. Comput. Sci. Inf. Syst. 16(2): 565-595 (2019) - [j26]Jadson Castro Gertrudes
A unified view of density-based methods for semi-supervised clustering and classification. Data Min. Knowl. Discov. 33(6): 1894-1952 (2019) - [c58]Félix Iglesias
Are Network Attacks Outliers? A Study of Space Representations and Unsupervised Algorithms. PKDD/ECML Workshops (2) 2019: 159-175 - [c57]Eirini Ntoutsi, Erich Schubert
1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2019). EDML@SDM 2019: 1-3 - [c56]Ruben Becker
Subspace Determination Through Local Intrinsic Dimensional Decomposition. SISAP 2019: 281-289 - [e3]Eirini Ntoutsi, Erich Schubert
Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning co-located with SIAM International Conference on Data Mining (SDM 2019), Calgary, Alberta, Canada, May 4th, 2019. CEUR Workshop Proceedings 2436, CEUR-WS.org 2019 [contents] - [i4]Erich Schubert, Arthur Zimek
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg". CoRR abs/1902.03616 (2019) - [i3]Ruben Becker, Imane Hafnaoui, Michael E. Houle, Pan Li, Arthur Zimek:
Subspace Determination through Local Intrinsic Dimensional Decomposition: Theory and Experimentation. CoRR abs/1907.06771 (2019) - 2018
- [j25]Arthur Zimek
There and back again: Outlier detection between statistical reasoning and data mining algorithms. WIREs Data Mining Knowl. Discov. 8(6) (2018) - [c55]Youcef Djenouri
Outlier Detection in Urban Traffic Flow Distributions. ICDM 2018: 935-940 - [c54]Félix Iglesias Vázquez
Outlier Detection Based on Low Density Models. ICDM Workshops 2018: 970-979 - [c53]Guilherme Oliveira Campos, Arthur Zimek
An Unsupervised Boosting Strategy for Outlier Detection Ensembles. PAKDD (1) 2018: 564-576 - [c52]Michael E. Houle, Erich Schubert
On the Correlation Between Local Intrinsic Dimensionality and Outlierness. SISAP 2018: 177-191 - [c51]Jadson Castro Gertrudes, Arthur Zimek
A unified framework of density-based clustering for semi-supervised classification. SSDBM 2018: 11:1-11:12 - [c50]Youcef Djenouri
Outlier Detection in Urban Traffic Data. WIMS 2018: 3:1-3:12 - [c49]Guilherme Oliveira Campos, Wagner Meira Jr., Arthur Zimek
Outlier Detection in Graphs: On the Impact of Multiple Graph Models. WIMS 2018: 21:1-21:12 - [r3]Peer Kröger, Arthur Zimek:
Subspace Clustering Techniques. Encyclopedia of Database Systems (2nd ed.) 2018 - [r2]Arthur Zimek, Erich Schubert
Outlier Detection. Encyclopedia of Database Systems (2nd ed.) 2018 - 2017
- [j24]Hans-Peter Kriegel, Erich Schubert
The (black) art of runtime evaluation: Are we comparing algorithms or implementations? Knowl. Inf. Syst. 52(2): 341-378 (2017) - [j23]Guillaume Casanova, Elias Englmeier, Michael E. Houle, Peer Kröger, Michael Nett, Erich Schubert
Dimensional Testing for Reverse k-Nearest Neighbor Search. Proc. VLDB Endow. 10(7): 769-780 (2017) - [c48]Daniel Basaran, Eirini Ntoutsi, Arthur Zimek
Redundancies in Data and their Effect on the Evaluation of Recommendation Systems: A Case Study on the Amazon Reviews Datasets. SDM 2017: 390-398 - [c47]Evelyn Kirner, Erich Schubert
Good and Bad Neighborhood Approximations for Outlier Detection Ensembles. SISAP 2017: 173-187 - 2016
- [j22]Guilherme Oliveira Campos, Arthur Zimek
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min. Knowl. Discov. 30(4): 891-927 (2016) - [j21]Pablo A. Jaskowiak
On strategies for building effective ensembles of relative clustering validity criteria. Knowl. Inf. Syst. 47(2): 329-354 (2016) - [j20]Ira Assent, Carlotta Domeniconi, Francesco Gullo, Andrea Tagarelli, Arthur Zimek
MultiClust 2013: Multiple Clusterings, Multiview Data, and Multisource Knowledgedriven Clustering: [Workshop Report]. SIGKDD Explor. 18(1): 35-38 (2016) - [c46]Lorne Swersky, Henrique O. Marques
On the Evaluation of Outlier Detection and One-Class Classification Methods. DSAA 2016: 1-10 - 2015
- [j19]Arthur Zimek
The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives. Mach. Learn. 98(1-2): 121-155 (2015) - [j18]Erich Schubert
A Framework for Clustering Uncertain Data. Proc. VLDB Endow. 8(12): 1976-1979 (2015) - [j17]Ricardo J. G. B. Campello
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Trans. Knowl. Discov. Data 10(1): 5:1-5:51 (2015) - [c45]Erich Schubert
Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles. DASFAA (2) 2015: 19-36 - [c44]Erich Schubert
Outlier Detection and Trend Detection: Two Sides of the Same Coin. ICDM Workshops 2015: 40-46 - [c43]Henrique O. Marques
On the internal evaluation of unsupervised outlier detection. SSDBM 2015: 7:1-7:12 - [i2]Laurent Amsaleg, Michael E. Houle, Vincent Oria, Arthur Zimek:
Dimensionality and Scalability II: Hands-On Intrinsic Dimensionality (NII Shonan Meeting 2015-9). NII Shonan Meet. Rep. 2015 (2015) - 2014
- [j16]Erich Schubert
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min. Knowl. Discov. 28(1): 190-237 (2014) - [c42]Jundong Li, Jörg Sander, Ricardo J. G. B. Campello
Active Learning Strategies for Semi-Supervised DBSCAN. Canadian AI 2014: 179-190 - [c41]Mojgan Pourrajabi, Davoud Moulavi, Ricardo J. G. B. Campello, Arthur Zimek
Model Selection for Semi-Supervised Clustering. EDBT 2014: 331-342 - [c40]Xuan-Hong Dang, Ira Assent
Discriminative features for identifying and interpreting outliers. ICDE 2014: 88-99 - [c39]Andreas Züfle, Tobias Emrich, Klaus Arthur Schmid, Nikos Mamoulis, Arthur Zimek
Representative clustering of uncertain data. KDD 2014: 243-252 - [c38]Erich Schubert
Generalized Outlier Detection with Flexible Kernel Density Estimates. SDM 2014: 542-550 - [c37]Davoud Moulavi, Pablo A. Jaskowiak
Density-Based Clustering Validation. SDM 2014: 839-847 - [c36]Arthur Zimek
Data perturbation for outlier detection ensembles. SSDBM 2014: 13:1-13:12 - [p2]Arthur Zimek
Frequent Pattern Mining Algorithms for Data Clustering. Frequent Pattern Mining 2014: 403-423 - 2013
- [j15]Kelvin Sim, Vivekanand Gopalkrishnan, Arthur Zimek
A survey on enhanced subspace clustering. Data Min. Knowl. Discov. 26(2): 332-397 (2013) - [j14]Ricardo J. G. B. Campello
A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Min. Knowl. Discov. 27(3): 344-371 (2013) - [j13]Arthur Zimek
Ensembles for unsupervised outlier detection: challenges and research questions a position paper. SIGKDD Explor. 15(1): 11-22 (2013) - [c35]Arthur Zimek
Subsampling for efficient and effective unsupervised outlier detection ensembles. KDD 2013: 428-436 - [c34]Elke Achtert, Hans-Peter Kriegel, Erich Schubert
Interactive data mining with 3D-parallel-coordinate-trees. SIGMOD Conference 2013: 1009-1012 - [c33]Erich Schubert
Geodetic Distance Queries on R-Trees for Indexing Geographic Data. SSTD 2013: 146-164 - [p1]Arthur Zimek:
Clustering High-Dimensional Data. Data Clustering: Algorithms and Applications 2013: 201-230 - [e2]Ira Assent, Carlotta Domeniconi, Francesco Gullo, Andrea Tagarelli, Arthur Zimek:
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering, in conjunction with KDD 2013, Chicago, IL, USA, August 11, 2013. ACM 2013, ISBN 978-1-4503-2334-5 [contents] - [i1]Michael E. Houle, Vincent Oria, Arthur Zimek:
Dimensionality and Scalability (NII Shonan Meeting 2013-4). NII Shonan Meet. Rep. 2013 (2013) - 2012
- [j12]Arthur Zimek
A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min. 5(5): 363-387 (2012) - [j11]Hans-Peter Kriegel, Peer Kröger, Arthur Zimek
Subspace clustering. WIREs Data Mining Knowl. Discov. 2(4): 351-364 (2012) - [c32]Elke Achtert, Sascha Goldhofer, Hans-Peter Kriegel, Erich Schubert
Evaluation of Clusterings - Metrics and Visual Support. ICDE 2012: 1285-1288 - [c31]Hans-Peter Kriegel, Peer Kröger, Erich Schubert
Outlier Detection in Arbitrarily Oriented Subspaces. ICDM 2012: 379-388 - [c30]Irene Ntoutsi, Arthur Zimek
Density-based Projected Clustering over High Dimensional Data Streams. SDM 2012: 987-998 - [c29]Erich Schubert
On Evaluation of Outlier Rankings and Outlier Scores. SDM 2012: 1047-1058 - [e1]Emmanuel Müller, Thomas Seidl, Suresh Venkatasubramanian, Arthur Zimek:
3rd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings, MultiClust '12, in conjunction with SDM 2012, Anaheim, CA, USA, April 28, 2012, Anaheim, CA, USA, April 28, 2012. SIAM 2012 [contents] - 2011
- [j10]Hans-Peter Kriegel, Peer Kröger, Jörg Sander
Density-based clustering. WIREs Data Mining Knowl. Discov. 1(3): 231-240 (2011) - [c28]Jilles Vreeken, Arthur Zimek:
When Pattern Met Subspace Cluster. MultiClust@ECML/PKDD 2011: 7-18 - [c27]Hans-Peter Kriegel, Erich Schubert
Evaluation of Multiple Clustering Solutions. MultiClust@ECML/PKDD 2011: 55-66 - [c26]Hans-Peter Kriegel, Peer Kröger, Erich Schubert
Interpreting and Unifying Outlier Scores. SDM 2011: 13-24 - [c25]Thomas Bernecker, Michael E. Houle, Hans-Peter Kriegel, Peer Kröger, Matthias Renz, Erich Schubert
Quality of Similarity Rankings in Time Series. SSTD 2011: 422-440 - [c24]Elke Achtert, Ahmed Hettab, Hans-Peter Kriegel, Erich Schubert
Spatial Outlier Detection: Data, Algorithms, Visualizations. SSTD 2011: 512-516 - [c23]Hans-Peter Kriegel, Peer Kröger, Irene Ntoutsi, Arthur Zimek
Density Based Subspace Clustering over Dynamic Data. SSDBM 2011: 387-404 - 2010
- [j9]Johannes Aßfalg, Jing Gong, Hans-Peter Kriegel, Alexey Pryakhin, Tiandi Wei, Arthur Zimek
Investigating a Correlation between Subcellular Localization and Fold of Proteins. J. Univers. Comput. Sci. 16(5): 604-621 (2010) - [j8]Arthur Zimek
A Study of Hierarchical and Flat Classification of Proteins. IEEE ACM Trans. Comput. Biol. Bioinform. 7(3): 563-571 (2010) - [c22]Elke Achtert, Hans-Peter Kriegel, Lisa Reichert, Erich Schubert
Visual Evaluation of Outlier Detection Models. DASFAA (2) 2010: 396-399 - [c21]Kyoji Kawagoe, Thomas Bernecker, Hans-Peter Kriegel, Matthias Renz, Arthur Zimek
Similarity Search in Time Series of Dynamical Model-based Systems. DEXA Workshops 2010: 110-114 - [c20]Thomas Bernecker, Tobias Emrich, Franz Graf, Hans-Peter Kriegel, Peer Kröger, Matthias Renz, Erich Schubert
Subspace similarity search using the ideas of ranking and top-k retrieval. ICDE Workshops 2010: 4-9 - [c19]Hans-Peter Kriegel, Peer Kröger, Irene Ntoutsi, Arthur Zimek
Towards subspace clustering on dynamic data: an incremental version of PreDeCon. StreamKDD@KDD 2010: 31-38 - [c18]Michael E. Houle, Hans-Peter Kriegel, Peer Kröger, Erich Schubert
Can Shared-Neighbor Distances Defeat the Curse of Dimensionality? SSDBM 2010: 482-500 - [c17]Thomas Bernecker, Tobias Emrich, Franz Graf, Hans-Peter Kriegel, Peer Kröger, Matthias Renz, Erich Schubert
Subspace Similarity Search: Efficient k-NN Queries in Arbitrary Subspaces. SSDBM 2010: 555-564
2000 – 2009
- 2009
- [j7]Johannes Aßfalg, Jing Gong, Hans-Peter Kriegel, Alexey Pryakhin, Tiandi Wei, Arthur Zimek
Supervised Ensembles of Prediction Methods for Subcellular Localization. J. Bioinform. Comput. Biol. 7(2): 269-285 (2009) - [j6]Gabriela Moise, Arthur Zimek
Subspace and projected clustering: experimental evaluation and analysis. Knowl. Inf. Syst. 21(3): 299-326 (2009) - [j5]Arthur Zimek
Correlation clustering. SIGKDD Explor. 11(1): 53-54 (2009) - [j4]Hans-Peter Kriegel, Peer Kröger, Arthur Zimek
Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Trans. Knowl. Discov. Data 3(1): 1:1-1:58 (2009) - [c16]Hans-Peter Kriegel, Peer Kröger, Erich Schubert
LoOP: local outlier probabilities. CIKM 2009: 1649-1652 - [c15]Hans-Peter Kriegel, Peer Kröger, Erich Schubert
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. PAKDD 2009: 831-838 - [c14]Elke Achtert, Thomas Bernecker, Hans-Peter Kriegel, Erich Schubert
ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series. SSTD 2009: 436-440 - [r1]Peer Kröger, Arthur Zimek
Subspace Clustering Techniques. Encyclopedia of Database Systems 2009: 2873-2875 - 2008
- [j3]Hans-Peter Kriegel, Peer Kröger, Arthur Zimek
Detecting clusters in moderate-to-high dimensional data: subspace clustering, pattern-based clustering, and correlation clustering. Proc. VLDB Endow. 1(2): 1528-1529 (2008) - [j2]Elke Achtert, Christian Böhm, Jörn David, Peer Kröger, Arthur Zimek
Global Correlation Clustering Based on the Hough Transform. Stat. Anal. Data Min. 1(3): 111-127 (2008) - [c13]Johannes Aßfalg, Jing Gong, Hans-Peter Kriegel, Alexey Pryakhin, Tiandi Wei, Arthur Zimek:
Supervised Ensembles of Prediction Methods for Subcellular Localization. APBC 2008: 29-38 - [c12]Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek
Angle-based outlier detection in high-dimensional data. KDD 2008: 444-452 - [c11]Elke Achtert, Christian Böhm, Jörn David, Peer Kröger, Arthur Zimek
Robust Clustering in Arbitrarily Oriented Subspaces. SDM 2008: 763-774 - [c10]Hans-Peter Kriegel, Peer Kröger, Erich Schubert
A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms. SSDBM 2008: 418-435 - [c9]Elke Achtert, Hans-Peter Kriegel, Arthur Zimek
ELKI: A Software System for Evaluation of Subspace Clustering Algorithms. SSDBM 2008: 580-585 - 2007
- [j1]Hans-Peter Kriegel, Karsten M. Borgwardt
Future trends in data mining. Data Min. Knowl. Discov. 15(1): 87-97 (2007) - [c8]Elke Achtert, Christian Böhm, Hans-Peter Kriegel, Peer Kröger, Ina Müller-Gorman, Arthur Zimek
Detection and Visualization of Subspace Cluster Hierarchies. DASFAA 2007: 152-163 - [c7]Elke Achtert, Christian Böhm, Hans-Peter Kriegel, Peer Kröger, Arthur Zimek
Robust, Complete, and Efficient Correlation Clustering. SDM 2007: 413-418 - [c6]Elke Achtert, Christian Böhm, Hans-Peter Kriegel, Peer Kröger, Arthur Zimek
On Exploring Complex Relationships of Correlation Clusters. SSDBM 2007: 7 - 2006
- [c5]Hans-Peter Kriegel, Alexey Pryakhin, Matthias Schubert, Arthur Zimek
COSMIC: Conceptually Specified Multi-Instance Clusters. ICDM 2006: 917-921 - [c4]Elke Achtert, Christian Böhm, Hans-Peter Kriegel, Peer Kröger, Arthur Zimek
Deriving quantitative models for correlation clusters. KDD 2006: 4-13 - [c3]Elke Achtert, Christian Böhm, Hans-Peter Kriegel, Peer Kröger, Ina Müller-Gorman, Arthur Zimek
Finding Hierarchies of Subspace Clusters. PKDD 2006: 446-453 - [c2]Elke Achtert, Christian Böhm, Peer Kröger, Arthur Zimek
Mining Hierarchies of Correlation Clusters. SSDBM 2006: 119-128 - 2004
- [c1]Christian Böhm, Karin Kailing, Peer Kröger, Arthur Zimek
Computing Clusters of Correlation Connected Objects. SIGMOD Conference 2004: 455-466
Coauthor Index
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