Scientific Work Using or Referencing ELKI

Over the years, ELKI has been increasingly cited/used in scientific publications and other software projects.

The following list is automatically generated from very heterogenous sources, and may contain errors. Where possible, we try to use metadata from DBLP,, OpenCitations, SemanticScholar, Microsoft Academic Search, and HTML meta headers from the publisher web pages. For theses, seminar articles etc. this approach does however not work. We have not verified every citation discovered by the bot.


  1. Piotr A. Kowalski, Szymon Łukasik, Małgorzata Charytanowicz, and Piotr Kulczycki (2019). Nature Inspired Clustering – Use Cases of Krill Herd Algorithm and Flower Pollination Algorithm. Interactions Between Computational Intelligence and Mathematics Part 2, 83-98, Springer, 10.1007/978-3-030-01632-6_6
  2. Dipesh Pradhan, and Feroz Zahid (2019). Data Center Clustering for Geographically Distributed Cloud Deployments. AINA Workshops, 1030-1040, Springer, 10.1007/978-3-030-15035-8_101
  3. Sudarshan S. Chawathe (2019). Clustering Blockchain Data. Unsupervised and Semi-Supervised Learning, 43-72, Springer, 10.1007/978-3-319-97864-2_3
  4. Dilip Singh Sisodia, Rahul Borkar, and Hari Shrawgi (2019). Performance Evaluation of Large Data Clustering Techniques on Web Robot Session Data. Machine Intelligence and Signal Analysis, 545-553, Springer, 10.1007/978-981-13-0923-6_47
  5. Bilkis Jamal Ferdosi, and Muhammad Masud Tarek (2019). Visual Verification and Analysis of Outliers Using Optimal Outlier Detection Result by Choosing Proper Algorithm and Parameter. Emerging Technologies in Data Mining and Information Security, 507-517, Springer, 10.1007/978-981-13-1498-8_45
  6. Liefa Liao, and Bin Luo (2019). Entropy Isolation Forest Based on Dimension Entropy for Anomaly Detection. Computational Intelligence and Intelligent Systems, 365-376, Springer, 10.1007/978-981-13-6473-0_32
  7. Ibrahim Aljarah, Majdi Mafarja, Ali Asghar Heidari, Hossam Faris, and Seyedali Mirjalili (2019). Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowledge and Information Systems, Springer, 10.1007/s10115-019-01358-x
  8. Dante Travisany, Eric Goles, Mauricio Latorre, María-Paz Cortés, and Alejandro Maass (2019). Generation and robustness of Boolean networks to model Clostridium difficile infection. Natural Computing, Springer, 10.1007/s11047-019-09730-0
  9. Łukasz Struski, Przemysław Spurek, Jacek Tabor, and Marek Śmieja (2019). Projected memory clustering. Pattern Recognition Letters, Elsevier BV, 10.1016/j.patrec.2019.02.023
  10. Julian Oehling, and David J. Barry (2019). Using machine learning methods in airline flight data monitoring to generate new operational safety knowledge from existing data. Safety Science 114, 89-104, Elsevier BV, 10.1016/j.ssci.2018.12.018
  11. Pedro Henrique B. Ruas, Alan D. Machado, Michelle C. Silva, Magali R. G. Meireles, Ana Maria P. Cardoso, Luis E. Zárate, and Cristiane N. Nobre (2019). Identification and characterisation of Facebook user profiles considering interaction aspects. Behaviour & Information Technology, 1-15, Informa UK Limited, 10.1080/0144929X.2019.1566498
  12. Luisa Sanz-Martínez, Erkan Er, Alejandra Martínez-Monés, Yannis Dimitriadis, and Miguel L. Bote-Lorenzo (2019). Creating collaborative groups in a MOOC: a homogeneous engagement grouping approach. Behaviour & Information Technology, 1-15, Informa UK Limited, 10.1080/0144929X.2019.1571109
  13. Hao Wang, Yan Yang, and Bing Liu (2019). GMC: Graph-based Multi-view Clustering. IEEE Transactions on Knowledge and Data Engineering, IEEE, 10.1109/TKDE.2019.2903810
  14. Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, and Xiangnan He (2019). Generative Adversarial Active Learning for Unsupervised Outlier Detection. IEEE Transactions on Knowledge and Data Engineering, IEEE, 10.1109/TKDE.2019.2905606
  15. Kaize Ding, Jundong Li, and Huan Liu (2019). Interactive Anomaly Detection on Attributed Networks. WSDM, 357-365, ACM, 10.1145/3289600.3290964, BibTeX
  16. Xiaodan Xu, Huawen Liu, and Minghai Yao (2019). Recent Progress of Anomaly Detection. Complexity 2019, 2686378:1-2686378:11, 10.1155/2019/2686378, BibTeX
  17. Junpeng Wang, Xiaotong Liu, and Han-Wei Shen (2019). High-dimensional data analysis with subspace comparison using matrix visualization. Information Visualization 18(1), 10.1177/1473871617733996, BibTeX
  18. Sanna Aronsson, Henrik Artman, Sinna Lindquist, Mikael Mitchell, Tomas Persson, Robert Ramberg, Mario Romero, and Pontus ter Vehn (2019). Supporting after action review in simulator mission training: Co-creating visualization concepts for training of fast-jet fighter pilots. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 154851291882329, SAGE Publications, 10.1177/1548512918823296
  19. Lukas Hardi, and Ulrich Wagner (2019). Grocery Delivery or Customer Pickup—Influences on Energy Consumption and CO2 Emissions in Munich. Sustainability 11(3), 641, Mdpi Ag, 10.3390/su11030641
  20. Yue Zhao, Zain Nasrullah, and Zheng Li (2019). PyOD: A Python Toolbox for Scalable Outlier Detection. CoRR abs/1901.01588, BibTeX
  21. Erich Schubert, and Arthur Zimek (2019). ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 “Heidelberg”. CoRR abs/1902.03616, BibTeX
  22. Benjamin Heinzerling (2019). Aspects of Coherence for Entity Analysis.
  23. Emeli Pettersson, and Albin Carlson (2019). Att hitta en nål i en höstack: Metoder och tekniker för att sålla och gradera stora mängder ostrukturerad textdata (Finding a Needle in a Haystack: Methods and techniques for screening and grading large amounts of unstructured textual data). Malmö universitet/Teknik och samhälle
  24. José David Jácome Escobar, and Estalin Augusto Viracocha Andrade (2019). Desarrollo de una aplicación para detección de patrones en imágenes mediante el uso de aprendizaje profundo. Quito: UCE
  25. Massimiliano de Leoni (2019). From Low-Level Events to Activities – A Session-Based Approach (Extended Version). arXiv 1903.03993


  1. Arthur Zimek, and Peter Filzmoser (2018). There and back again: Outlier detection between statistical reasoning and data mining algorithms. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8(6), 10.1002/widm.1280, BibTeX
  2. Mark Wickham (2018). Machine Learning Environments. Practical Java Machine Learning, 227-295, Apress, 10.1007/978-1-4842-3951-3_5
  3. Huixiao Hong, Jieqiang Zhu, Minjun Chen, Ping Gong, Chaoyang Zhang, and Weida Tong (2018). Quantitative Structure–Activity Relationship Models for Predicting Risk of Drug-Induced Liver Injury in Humans. Drug-Induced Liver Toxicity, 77-100, Springer, 10.1007/978-1-4939-7677-5_5
  4. Adam Byron (2018). Proteomic Profiling of Integrin Adhesion Complex Assembly. Methods in Molecular Biology, 193-236, Springer, 10.1007/978-1-4939-7759-8_13
  5. Michael E. Houle, Erich Schubert, and Arthur Zimek (2018). On the Correlation Between Local Intrinsic Dimensionality and Outlierness. SISAP, 177-191, Springer, 10.1007/978-3-030-02224-2_14, BibTeX
  6. Michael Richter, Jürgen Hermes, and Claes Neuefeind (2018). Aspectual Classifications: Use of Raters’ Associations and Co-occurrences of Verbs for Aspectual Classification in German. ICAART (Revised Selected Papers), 467-491, Springer, 10.1007/978-3-030-05453-3_22, BibTeX
  7. Arno G. Stefani, Achim Sandmann, Andreas Burkovski, Johannes B. Huber, Heinrich Sticht, and Christophe Jardin (2018). Application of Methods from Information Theory in Protein-Interaction Analysis. Lecture Notes in Bioengineering, 293-313, Springer, 10.1007/978-3-319-54729-9_13
  8. Helmut Neukirchen (2018). Elephant Against Goliath: Performance of Big Data Versus High-Performance Computing DBSCAN Clustering Implementations. Simulation Science, 251-271, Springer, 10.1007/978-3-319-96271-9_16
  9. Meiling Zhu, Chen Liu, and Yanbo Han (2018). An Event Correlation Based Approach to Predictive Maintenance. APWeb/WAIM (2), 232-247, Springer, 10.1007/978-3-319-96893-3_18, BibTeX
  10. Alberto Fernández, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, and Francisco Herrera (2018). Data Intrinsic Characteristics. Learning from Imbalanced Data Sets, 253-277, Springer, 10.1007/978-3-319-98074-4_10
  11. Salma Jamal, Sukriti Goyal, Abhinav Grover, and Asheesh Shanker (2018). Machine Learning: What, Why, and How?. Bioinformatics: Sequences, Structures, Phylogeny, 359-374, Springer, 10.1007/978-981-13-1562-6_16
  12. Daniyal Kazempour, Markus Mauder, Peer Kröger, and Thomas Seidl (2018). Detecting global hyperparaboloid correlated clusters: a Hough-transform based multicore algorithm. Distributed and Parallel Databases, Springer Nature America, Inc, 10.1007/s10619-018-7246-0
  13. Ingmar Wiese, Nicole Sarna, Lena Wiese, Araek Tashkandi, and Ulrich Sax (2018). Concept acquisition and improved in-database similarity analysis for medical data. Distributed and Parallel Databases, Springer Nature America, Inc, 10.1007/s10619-018-7249-x
  14. Ahmed AlEroud, and Aryya Gangopadhyay (2018). Multimode co-clustering for analyzing terrorist networks. Information Systems Frontiers 20(5), 1053-1074, 10.1007/s10796-016-9712-4, BibTeX
  15. Jakub Sawicki, Marcin Los, Maciej Smolka, Robert Schaefer, and Julen Álvarez-Aramberri (2018). Approximating landscape insensitivity regions in solving ill-conditioned inverse problems. Memetic Computing 10(3), 279-289, 10.1007/s12293-018-0258-5, BibTeX
  16. Zahid Halim, and Jamal Hussain Khattak (2018). Density-based clustering of big probabilistic graphs. Evolving Systems, Springer, 10.1007/s12530-018-9223-2
  17. Haofan Zhang, Ke Nian, Thomas F. Coleman, and Yuying Li (2018). Spectral ranking and unsupervised feature selection for point, collective, and contextual anomaly detection. International Journal of Data Science and Analytics, Springer, 10.1007/s41060-018-0161-7
  18. Joice K. Joseph, Karunakaran Akhil Dev, A.P. Pradeepkumar, and Mahesh Mohan (2018). Big Data Analytics and Social Media in Disaster Management. Integrating Disaster Science and Management, 287-294, Elsevier, 10.1016/B978-0-12-812056-9.00016-6
  19. Anitha Ramchandran, and Arun Kumar Sangaiah (2018). Unsupervised Anomaly Detection for High Dimensional Data—an Exploratory Analysis. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, 233-251, Elsevier, 10.1016/B978-0-12-813314-9.00011-6
  20. C. Yilmaz, C. Akalin, I. Gunal, H. Celik, Murat Buyuk, A. Suleman, and M. Yildiz (2018). A hybrid damage assessment for E-and S-glass reinforced laminated composite structures under in-plane shear loading. Composite Structures 186, 347-354, Elsevier BV, 10.1016/j.compstruct.2017.12.023
  21. Yannis Papanikolaou, Grigorios Tsoumakas, and Ioannis Katakis (2018). Hierarchical partitioning of the output space in multi-label data. Data Knowl. Eng. 116, 42-60, 10.1016/j.datak.2018.05.003, BibTeX
  22. Andreas Solti, Manuel Raffel, Giovanni Romagnoli, and Jan Mendling (2018). Misplaced product detection using sensor data without planograms. Decision Support Systems 112, 76-87, 10.1016/j.dss.2018.06.006, BibTeX
  23. Sarah Shukri, Hossam Faris, Ibrahim Aljarah, Seyedali Mirjalili, and Ajith Abraham (2018). Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer. Eng. Appl. of AI 72, 54-66, 10.1016/j.engappai.2018.03.013, BibTeX
  24. Mohammad Khan Afridi, Nouman Azam, JingTao Yao, and Eisa Alanazi (2018). A three-way clustering approach for handling missing data using GTRS. Int. J. Approx. Reasoning 98, 11-24, 10.1016/j.ijar.2018.04.001, BibTeX
  25. Jonathan R. Wells, and Kai Ming Ting (2018). A new simple and efficient density estimator that enables fast systematic search. Pattern Recognition Letters, Elsevier BV, 10.1016/j.patrec.2018.12.020
  26. Kummerow André, Nicolai Steffen, and Bretschneider Peter (2018). Outlier Detection Methods for Uncovering of Critical Events in Historical Phasor Measurement Records. E3S Web of Conferences 64, 08006, EDP Sciences, 10.1051/e3sconf/20186408006
  27. Wenying Ji, Simaan M. AbouRizk, Osmar R. Zaïane, and Yitong Li (2018). Complexity Analysis Approach for Prefabricated Construction Products Using Uncertain Data Clustering. Journal of Construction Engineering and Management 144(8), 04018063, American Society of Civil Engineers (ASCE), 10.1061/(asce)co.1943-7862.0001520
  28. S A Rylov, and I A Pestunov (2018). Fast hierarchical clustering of multispectral images and its implementation on NVIDIA GPU. Journal of Physics: Conference Series 1096, 012039, IOP Publishing, 10.1088/1742-6596/1096/1/012039
  29. Omid Rajabi Shishvan, Daphney-Stavroula Zois, and Tolga Soyata (2018). Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey. IEEE Access 6, 46419-46494, 10.1109/ACCESS.2018.2866049, BibTeX
  30. Xuan-Hong Dang, Raji Akella, Somaieh Bahrami, Vadim Sheinin, and Petros Zerfos (2018). Unsupervised Threshold Autoencoder to Analyze and Understand Sentence Elements. BigData, 3267-3276, IEEE, 10.1109/BigData.2018.8622379, BibTeX
  31. Yanbo Han, Meiling Zhu, and Chen Liu (2018). A Service-Oriented Approach to Modeling and Reusing Event Correlations. COMPSAC (1), 498-507, IEEE, 10.1109/COMPSAC.2018.00077, BibTeX
  32. Jingyu Sun, Masato Kamiya, and Susumu Takeuchi (2018). Introducing Hierarchical Clustering with Real Time Stream Reasoning into Semantic-Enabled IoT. COMPSAC (2), 540-545, IEEE, 10.1109/COMPSAC.2018.10291, BibTeX
  33. Cédric Buche, Cindy Even, and Julien Soler (2018). Autonomous Virtual Player in a Video Game Imitating Human Players: The ORION Framework. CW, 108-113, IEEE, 10.1109/CW.2018.00029, BibTeX
  34. Pavol Mulinka, Pedro Casas, and Lukas Kencl (2018). Hi-Clust: Unsupervised Analysis of Cloud Latency Measurements Through Hierarchical Clustering. CloudNet, 1-7, IEEE, 10.1109/CloudNet.2018.8549558, BibTeX
  35. Dilip Singh Sisodia, Radhika Khandelwal, and Arti Anuragi (2018). Categorization Performance of Unsupervised Learning Techniques for Web Robots Sessions. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, 10.1109/ICIRCA.2018.8597200
  36. Dilip Singh Sisodia, and Akanksha Verma (2018). Performance of Unsupervised Learning Algorithms for Online Document Clustering. 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, 10.1109/ICIRCA.2018.8597378
  37. Andre Kummerow, Steffen Nicolai, and Peter Bretschneider (2018). Ensemble approach for automated extraction of critical events from mixed historical PMU data sets. 2018 IEEE Power & Energy Society General Meeting (PESGM), IEEE, 10.1109/PESGM.2018.8586641
  38. Tommaso Zoppi, Andrea Ceccarelli, and Andrea Bondavalli (2018). On Algorithms Selection for Unsupervised Anomaly Detection. PRDC, 279-288, IEEE, 10.1109/PRDC.2018.00050, BibTeX
  39. Pinjia He, Jieming Zhu, Shilin He, Jian Li, and Michael R. Lyu (2018). Towards Automated Log Parsing for Large-Scale Log Data Analysis. IEEE Trans. Dependable Sec. Comput. 15(6), 931-944, 10.1109/TDSC.2017.2762673, BibTeX
  40. Punit Rathore, Dheeraj Kumar, James C. Bezdek, Sutharshan Rajasegarar, and Marimuthu S. Palaniswami (2018). A Rapid Hybrid Clustering Algorithm for Large Volumes of High Dimensional Data. IEEE Transactions on Knowledge and Data Engineering, IEEE, 10.1109/TKDE.2018.2842191
  41. Huawen Liu, Xuelong Li, Jiuyong Li, and Shichao Zhang (2018). Efficient Outlier Detection for High-Dimensional Data. IEEE Trans. Systems, Man, and Cybernetics: Systems 48(12), 2451-2461, 10.1109/TSMC.2017.2718220, BibTeX
  42. Yoshihiro Okada (2018). Time-Tunnel: 3D Visualization Tool and Its Aspects as 3D Parallel Coordinates. IV, 50-55, IEEE, 10.1109/iV.2018.00019, BibTeX
  43. Tharindu R. Bandaragoda, Kai Ming Ting, David W. Albrecht, Fei Tony Liu, Ye Zhu, and Jonathan R. Wells (2018). Isolation-based anomaly detection using nearest-neighbor ensembles. Computational Intelligence 34(4), 968-998, 10.1111/coin.12156, BibTeX
  44. Xiao Huang, Qingquan Song, Jundong Li, and Xia Hu (2018). Exploring Expert Cognition for Attributed Network Embedding. WSDM, 270-278, ACM, 10.1145/3159652.3159655, BibTeX
  45. Payam Karisani, and Eugene Agichtein (2018). Did You Really Just Have a Heart Attack?: Towards Robust Detection of Personal Health Mentions in Social Media. WWW, 137-146, ACM, 10.1145/3178876.3186055, BibTeX
  46. Chen Luo, and Anshumali Shrivastava (2018). Arrays of (locality-sensitive) Count Estimators (ACE): Anomaly Detection on the Edge. WWW, 1439-1448, ACM, 10.1145/3178876.3186056, BibTeX
  47. Dominik Mautz, Wei Ye, Claudia Plant, and Christian Böhm (2018). Discovering Non-Redundant K-means Clusterings in Optimal Subspaces. KDD, 1973-1982, ACM, 10.1145/3219819.3219945, BibTeX
  48. Erich Schubert, and Michael Gertz (2018). Numerically stable parallel computation of (co-)variance. SSDBM, 10:1-10:12, ACM, 10.1145/3221269.3223036, BibTeX
  49. Eva Tuba, Raka Jovanovic, Romana Capor-Hrosik, Adis Alihodzic, and Milan Tuba (2018). Web Intelligence Data Clustering by Bare Bone Fireworks Algorithm Combined with K-Means. WIMS, 7:1-7:8, ACM, 10.1145/3227609.3227650, BibTeX
  50. Youcef Djenouri, and Arthur Zimek (2018). Outlier Detection in Urban Traffic Data. WIMS, 3:1-3:12, ACM, 10.1145/3227609.3227692, BibTeX
  51. Firas Abuzaid, Peter Bailis, Jialin Ding, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, and Sahaana Suri (2018). MacroBase: Prioritizing Attention in Fast Data. ACM Trans. Database Syst. 43(4), 15:1-15:45, 10.1145/3276463, BibTeX
  52. 星野 綾子, and 細見 格 (2018). 句構造解析とクラスタリングを用いた会話履歴の要約. 人工知能学会全国大会論文集 第32回全国大会(2018), 2K102-2K102, 一般社団法人 人工知能学会, 10.11517/pjsai.JSAI2018.0_2K102
  53. Bastian Hornung, Vitor A. P. Martins dos Santos, Hauke Smidt, and Peter J. Schaap (2018). Studying microbial functionality within the gut ecosystem by systems biology. Genes & Nutrition 13(1), Springer, 10.1186/s12263-018-0594-6
  54. Sevvandi Kandanaarachchi, Mario Andrés Muñoz Acosta, Rob Hyndman, and Kate Smith-Miles (2018). On normalization and algorithm selection for unsupervised outlier detection. 10.13140/RG.2.2.19976.26880
  55. Wookey Lee, and Woong-Kee Loh (2018). G-OPTICS: fast ordering density-based cluster objects using graphics processing units. IJWGS 14(3), 273-287, 10.1504/IJWGS.2018.092583, BibTeX
  56. I.Y. Grishanova, , J.V. Rogushina, and (2018). Technological solutions for intelligent analysis of Big Data. Programming languages. Problems In Programming, 045-058, Co. Ltd. Ukrinformnauka, 10.15407/pp2018.04.045
  57. Xiaodan Xu, Huawen Liu, Li Li, and Minghai Yao (2018). A Comparison of Outlier Detection Techniques for High-Dimensional Data. Int. J. Comput. Intell. Syst. 11(1), 652-662, 10.2991/ijcis.11.1.50, BibTeX
  58. Meiling Zhu, and Chen Liu (2018). A Correlation Driven Approach with Edge Services for Predictive Industrial Maintenance. Sensors 18(6), 1844, 10.3390/s18061844, BibTeX
  59. Christian Sand, Tobias Lechler, Patricia Schuh, and Jörg Franke (2018). Potentials for Error Detection and Process Visualization in Assembly Lines Using a Parallel Coordinates Plot. Applied Mechanics and Materials 882, 10-16, Trans Tech Publications, 10.4028/
  60. Simon Ruske, David O. Topping, Virginia E. Foot, Andrew P. Morse, and Martin W. Gallagher (2018). Machine learning for improved data analysis of biological aerosol using the WIBS. Atmospheric Measurement Techniques Discussions, 1-19, Copernicus GmbH, 10.5194/amt-2018-126
  61. Jürgen Hermes, Michael Richter, and Claes Neuefeind (2018). Supervised Classification of Aspectual Verb Classes in German - Subcategorization-Frame-Based vs Window-Based Approach: A Comparison. ICAART (2), 653-662, SciTePress, 10.5220/0006728106530662, BibTeX
  62. Minh-Anh Le (2018). Anomaly Detection using Machine Learning Methods Implementation and Benchmark Analysis of Selected Methods and Tuning Criteria. 10.5282/ubm/epub.58320
  63. Vladimir Kurbalija, Mirjana Ivanović, Zoltan Geler, and Miloš Radovanović (2018). Two Faces of the Framework for Analysis and Prediction, Part 2 - Research. ITC 47(3), 489-502, 10.5755/j01.itc.47.3.18747, BibTeX
  64. Wenying Ji (2018). Simulation-Based Analytics for Fabrication Quality-Associated Decision Support. arXiv: Other Computer Science, 10.7939/R3HX16598
  65. Hong Yu, Tiantian Zhang, Yahong Lian, and Yu Cai (2018). Co-regularized Multi-view Subspace Clustering. ACML, 17-32,, BibTeX
  66. Erich Schubert, Andreas Spitz, and Michael Gertz (2018). Exploring Significant Interactions in Live News. NewsIR@ECIR, 39-44,, BibTeX
  67. Erich Schubert, and Michael Gertz (2018). Improving the Cluster Structure Extracted from OPTICS Plots. LWDA, 318-329,, BibTeX
  68. Stephen Pauwels, and Toon Calders (2018). Extending Dynamic Bayesian Networks for Anomaly Detection in Complex Logs. CoRR abs/1805.07107, BibTeX
  69. Fatemeh Riahi, and Oliver Schulte (2018). Model-based Exception Mining for Object-Relational Data. CoRR abs/1807.00381, BibTeX
  70. Roberto Pirrone, Vincenzo Cannella, Sergio Monteleone, and Gabriella Giordano (2018). Linear density-based clustering with a discrete density model. CoRR abs/1807.08158, BibTeX
  71. Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, and Xiangnan He (2018). Generative Adversarial Active Learning for Unsupervised Outlier Detection. CoRR abs/1809.10816, BibTeX
  72. Edouard Fouché, and Klemens Böhm (2018). Monte Carlo Dependency Estimation. CoRR abs/1810.02112, BibTeX
  73. Erich Schubert, and Peter J. Rousseeuw (2018). Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms. CoRR abs/1810.05691, BibTeX
  74. Wei Ye (2018). Data mining using concepts of independence, unimodality and homophily. Ludwig Maximilian University of Munich, Germany, BibTeX
  75. Slimane Oulad-Naoui (2018). Fouille de motifs: formalisation et unification. (Pattern Mining: Formalisation and Unification). University of Laghouat, Algeria, BibTeX
  76. Richard M. Reese, and AshishSingh Bhatia (2018). Natural Language Processing with Java. Techniques for building machine learning and neural network models for NLP, 2nd Edition. Packt Publishing Ltd, 9781788993067
  77. Alfonso Román y Zubeldia (2018). Implementación de pruebas para una hipótesis sobre la aplicación de distancia Euclidiana para realizar agrupamientos en espacios multidimensionales. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas
  78. Angie Cristina Pinto Chamorro, and Víctor Marcelo Zambrano Morocho (2018). Plataforma Tecnológica Para Contribuir La Planeación Urbana En La Ciudad De Guayaquil Dirigido A La Transportación, Enfocado A La Implementación De Algoritmos De Análisis De Trayectoria. Universidad de Guayaquil. Facultad de Ciencias Matemáticas y Físicas. Carrera de Ingeniería En Sistemas Computacionales
  79. Carlos Humberto Apolaya Torres, and Adolfo Espinosa Diaz (2018). Técnicas de inferencias, predicción y minería de datos. Universidad Peruana de Ciencias Aplicadas (UPC)
  80. Dušan HETLEROVIĆ (2018). Detekce anomálií v klasifikovaných datech: Vyhodnocování [online].
  81. Elvis Ricardo Tapia Aparicio (2018). Modelo de minería de datos para identificación de patrones que influyen en el aprovechamiento académico de la Carrera de Sistemas de Información de la Universidad de Guayaquil. Universidad de Guayaquil. Facultad de Ingeniería Industrial. Carrera de Licenciatura en Sistemas de Información.
  82. Fatih Kayaalp, and Muhammet Sinan Başarslan (2018). Open source data mining programs: a case study on R (Açık kaynak kodlu veri madenciliği programları: R’da örnek uygulama). Düzce University
  83. Gjergji Make (2018). Implementing KNIME Analytical Platform for visualizing data in educational context. Haaga-Helia ammattikorkeakoulu
  84. Luisa Sanz Martínez, Alejandra Martínez Monés, Miguel L. Bote Lorenzo, and Yannis A. Dimitriadis (2018). Validating performance of group formation based on homogeneous engagement criteria in MOOCs.
  85. Žemlička Radomír (2018). Spolupráce studentů různé úrovně znalostí. České vysoké učení technické v Praze. Vypočetní a informační centrum.


  1. Abdulrahman H. Altalhi, José María Luna, M. A. Vallejo, and Sebastián Ventura (2017). Evaluation and comparison of open source software suites for data mining and knowledge discovery. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 7(3), 10.1002/widm.1204, BibTeX
  2. Peer Kröger, and Arthur Zimek (2017). Subspace Clustering Techniques. Encyclopedia of Database Systems, 1-4, Springer, 10.1007/978-1-4899-7993-3_607-2
  3. Arthur Zimek, and Erich Schubert (2017). Outlier Detection. Encyclopedia of Database Systems, 1-5, Springer, 10.1007/978-1-4899-7993-3_80719-1
  4. Adam Byron (2017). Clustering and Network Analysis of Reverse Phase Protein Array Data. Molecular Profiling, 171-191, Springer, 10.1007/978-1-4939-6990-6_12
  5. Charu C. Aggarwal (2017). Applications of Outlier Analysis. Outlier Analysis, 399-422, Springer, 10.1007/978-3-319-47578-3_13
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  26. Arthur Zimek, Matthew Gaudet, Ricardo J. G. B. Campello, and Jörg Sander (2013). Subsampling for efficient and effective unsupervised outlier detection ensembles. KDD, 428-436, ACM, 10.1145/2487575.2487676, BibTeX
  27. Benjamin Welton, Evan Samanas, and Barton P. Miller (2013). Mr. Scan: extreme scale density-based clustering using a tree-based network of GPGPU nodes. SC, 84:1-84:11, ACM, 10.1145/2503210.2503262, BibTeX
  28. Johannes Schneider, and Michail Vlachos (2013). Fast parameterless density-based clustering via random projections. CIKM, 861-866, ACM, 10.1145/2505515.2505590, BibTeX
  29. Toon De Pessemier, Simon Dooms, and Luc Martens (2013). A food recommender for patients in a care facility. RecSys, 209-212, ACM, 10.1145/2507157.2507198, BibTeX
  30. David Ando, Michael Colvin, Michael Rexach, and Ajay Gopinathan (2013). Physical Motif Clustering within Intrinsically Disordered Nucleoporin Sequences Reveals Universal Functional Features. PLoS ONE 8(9), e73831, Public Library of Science (PLoS), 10.1371/journal.pone.0073831
  31. Martin Schäler, Alexander Grebhahn, Reimar Schröter, Sandro Schulze, Veit Köppen, and Gunter Saake (2013). QuEval: Beyond high-dimensional indexing a la carte. PVLDB 6(14), 1654-1665, 10.14778/2556549.2556551, BibTeX
  32. Martin Behnisch, Gotthard Meinel, Sebastian Tramsen, and Markus Diesselmann (2013). Using quadtree representations in building stock visualization and analysis. Erdkunde 67(2), 151-166, Erdkunde, 10.3112/erdkunde.2013.02.04
  33. Jai PrakashVerma, Bankim Patel, and Atul Patel (2013). Web Mining: Opinion and Feedback Analysis for Educational Institutions. International Journal of Computer Applications 84(6), 17-22, Foundation of Computer Science, 10.5120/14579-2800
  34. Charu C. Aggarwal, and Chandan K. Reddy (2013). Educational and Software Resources for Data Clustering. Data Clustering: Algorithms and Applications, 607-616, BibTeX
  35. Arthur Zimek (2013). Clustering High-Dimensional Data. Data Clustering: Algorithms and Applications, 201-230, BibTeX
  36. Tobias Emrich, Peer Kröger, Johannes Niedermayer, Matthias Renz, and Andreas Züfle (2013). A Mutual Pruning Approach for RkNN Join Processing. BTW, 21-35, GI, BibTeX
  37. Sylvain Dormieu, and Nicolas Labroche (2013). SNOW, un algorithme exploratoire pour le subspace clustering. EGC, 79-84, Hermann-Éditions, BibTeX
  38. Tobias Emrich (2013). Coping with distance and location dependencies in spatial, temporal and uncertain data. Ludwig Maximilians University Munich, BibTeX
  39. Daniel Kuntze (2013). Practical algorithms for clustering and modeling large data sets: analysis and improvements. 1-130, University of Paderborn, BibTeX
  40. Erich Schubert (2013). Generalized and efficient outlier detection for spatial, temporal, and high-dimensional data mining. 1-262, Ludwig Maximilians University Munich, BibTeX
  41. Andreas Züfle (2013). Similarity search and mining in uncertain spatial and spatio-temporal databases. 1-397, Ludwig Maximilians University Munich, BibTeX
  42. Matthew Orlinski (2013). Neighbour discovery and distributed spatio-temporal cluster detection in pocket switched networks. University of Manchester, UK, BibTeX
  43. Claire Elizabeth Q (2013). Machine learning analysis of the cultural and cross-cultural aspects of beauty in music. Aberystwyth University, UK, BibTeX
  44. Thomas H. Davenport, and Jinho Kim (2013). Keeping Up with the Quants. Your Guide to Understanding and Using Analytics. Harvard Business Press, 9781422187265
  45. Ivanka Menken (2013). Data Mining Guidance - Real World Application, Templates, Documents, and Examples of the use of Data Mining in the Public Domain. Emereo Publishing, 9781486460458
  46. Albrecht Zimmermann (2013). Feature construction based on class outliers. CW Reports
  47. Bruno Daigle (2013). Méthodes bioinformatiques pour l’évaluation de la classification du virus du papillome humain. Université du Québec à Montréal
  48. Curdin Barandun, Stefan Derungs, and Gino Paulaitis (2013). Mixtape: Analyse und Erstellung Ähnlichkeitsanalyse von Musik anhand einer praktischen Implementation. HSR Hochschule für Technik Rapperswil
  49. Hardy Kremer (2013). Mining and similarity search in temporal databases. Aachen, Techn. Hochsch., Diss., 2013
  50. Jan Vykopal (2013). Flow-based Brute-force Attack Detection in Large and High-speed Networks. Masarykova univerzita, Fakulta informatiky
  51. Jan Vykopal (2013). SimFlow - a similarity-based detection of brute-force attacks.
  52. Kai M Ting (2013). Second Generation of Mass Estimation. Monash Univ Churchill (Australia) Gippsland School Of Information Technology
  53. Luiz O. Carvalho, Thatyana F. P. Seraphim, Caetano Traina Júnior, and Enzo Seraphim (2013). ObInject: a NoODMG Persistence and Indexing Framework for Object Injection. Journal of Information and Data Management 4(3), 220
  54. Manish Gupta (2013). Outlier detection for information networks. University of Illinois at Urbana-Champaign
  55. Maria José Gomes Pedroto (2013). Estimação de Massa em Energia Eólica.
  56. N Ronald (2013). Workers, adventurers, explorers: uncovering activity patterns in Melbourne. Australasian Transport Research Forum (ATRF), 36th, 2013, Brisbane, Queensland, Australia
  57. Solen Quiniou, Peggy Cellier, Thierry Charnois, and Dominique Legallois (2013). Graph Mining under Linguistic Constraints to Explore Large Texts. International Conference on Intelligent Text Processing and Computational Linguistics (CICLing’13)
  58. Stefan Eduard Raposo Alves (2013). Towards improving WEBSOM with multi-word expressions. Faculdade de Ciências e Tecnologia
  59. Vladimír Matejovský (2013). Podpora shlukování webových stránek pomocí link mining. Masarykova univerzita, Fakulta informatiky


  1. Arthur Zimek, Erich Schubert, and Hans-Peter Kriegel (2012). A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining 5(5), 363-387, 10.1002/sam.11161, BibTeX
  2. Hans-Peter Kriegel, Peer Kröger, and Arthur Zimek (2012). Subspace clustering. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 2(4), 351-364, 10.1002/widm.1057, BibTeX
  3. Charu C. Aggarwal (2012). An Introduction to Outlier Analysis. Outlier Analysis, 1-40, Springer, 10.1007/978-1-4614-6396-2_1
  4. Dawn E. Holmes, Jeffrey Tweedale, and Lakhmi C. Jain (2012). Data Mining Techniques in Clustering, Association and Classification. Data Mining: Foundations and Intelligent Paradigms, 1-6, Springer, 10.1007/978-3-642-23166-7_1
  5. Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer, and Jesse Read (2012). Stream Data Mining Using the MOA Framework. DASFAA (2), 309-313, Springer, 10.1007/978-3-642-29035-0_27, BibTeX
  6. Ira Assent, Philipp Kranen, Corinna Baldauf, and Thomas Seidl (2012). AnyOut: Anytime Outlier Detection on Streaming Data. DASFAA (1), 228-242, Springer, 10.1007/978-3-642-29038-1_18, BibTeX
  7. eva Kühn, Alexander Marek, Thomas Scheller, Vesna Sesum-Cavic, Michael Vögler, and Stefan Craß (2012). A Space-Based Generic Pattern for Self-Initiative Load Clustering Agents. COORDINATION, 230-244, Springer, 10.1007/978-3-642-30829-1_16, BibTeX
  8. Emmanuel Müller, Fabian Keller, Sebastian Blanc, and Klemens Böhm (2012). OutRules: A Framework for Outlier Descriptions in Multiple Context Spaces. ECML/PKDD (2), 828-832, Springer, 10.1007/978-3-642-33486-3_57, BibTeX
  9. Mohamed Bouguessa (2012). Modeling Outlier Score Distributions. ADMA, 713-725, Springer, 10.1007/978-3-642-35527-1_59, BibTeX
  10. Boris Delibasic, Milan Vukicevic, Milos Jovanovic, Kathrin Kirchner, Johannes Ruhland, and Milija Suknovic (2012). An architecture for component-based design of representative-based clustering algorithms. Data Knowl. Eng. 75, 78-98, 10.1016/j.datak.2012.03.005, BibTeX
  11. Elke Achtert, Sascha Goldhofer, Hans-Peter Kriegel, Erich Schubert, and Arthur Zimek (2012). Evaluation of Clusterings - Metrics and Visual Support. ICDE, 1285-1288, IEEE, 10.1109/ICDE.2012.128, BibTeX
  12. Hans-Peter Kriegel, Peer Kröger, Erich Schubert, and Arthur Zimek (2012). Outlier Detection in Arbitrarily Oriented Subspaces. ICDM, 379-388, IEEE, 10.1109/ICDM.2012.21, BibTeX
  13. Mohamed Bouguessa (2012). A Probabilistic Combination Approach to Improve Outlier Detection. ICTAI, 666-673, IEEE, 10.1109/ICTAI.2012.95, BibTeX
  14. Monalisa Mandal, and Anirban Mukhopadhyay (2012). Identifying most relevant non-redundant gene markers from gene expression data using PSO-based graph -theoretic approach. 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing, 374-379, IEEE, 10.1109/PDGC.2012.6449849
  15. Erich Schubert, Remigius Wojdanowski, Arthur Zimek, and Hans-Peter Kriegel (2012). On Evaluation of Outlier Rankings and Outlier Scores. SDM, 1047-1058, SIAM / Omnipress, 10.1137/1.9781611972825.90, BibTeX
  16. Thomas Bernecker, Franz Graf, Hans-Peter Kriegel, Nepomuk Seiler, Christoph Türmer, and Dieter Dill (2012). Knowing: a generic data analysis application. EDBT, 630-633, ACM, 10.1145/2247596.2247683, BibTeX
  17. Stephan Günnemann, Ines Färber, Kittipat Virochsiri, and Thomas Seidl (2012). Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data. KDD, 352-360, ACM, 10.1145/2339530.2339588, BibTeX
  18. Linda Dib, and Alessandra Carbone (2012). CLAG: an unsupervised non hierarchical clustering algorithm handling biological data. BMC Bioinformatics 13, 194, 10.1186/1471-2105-13-194, BibTeX
  19. Thomas Bernecker (2012). Similarity processing in multi-observation data. 1-253, Ludwig Maximilian University of Munich, Germany, BibTeX
  20. Franz Graf (2012). Data and knowledge engineering for medical image and sensor data. 1-221, Ludwig Maximilian University of Munich, Germany, BibTeX
  21. Steffen Suchandt, and Hartmut Runge (2012). Along-track interferometry using TanDEM-X: First results from marine and land applications. EUSAR 2012; 9th European Conference on Synthetic Aperture Radar, 392-395, VDE, 978-3-8007-3404-7
  22. Bruno Tavares (2012). Sistema de recomendação para plataformas de e-learning. Instituto Politécnico do Porto. Instituto Superior de Engenharia do Porto
  23. E. B. Beuschau (2012). Learning usage behavior based on app feedback.
  24. Francesco Indaco (2012). Hierarchical Clustering Using Level Sets. San Jose State University
  25. Ilango Velchamy, R Subramanian, and V Vasudevan (2012). A Five Step Procedure for Outlier Analysis in Data Mining. European Journal of Scientific Research
  26. Jens Ehlers (2012). Self-Adaptive Performance Monitoring for Component-Based Software Systems. 252, Books on Demand GmbH
  27. Γρηγόριος Αθανασίου (2012). Business plan νέας ηλεκτρονικής επιχείρησης (Δημιουργία-Εφαρμογή). Πανεπιστήμιο Μακεδονίας Οικονομικών και Κοινωνικών Επιστημών
  28. Νικόλαος Δ. Γρίβας, and Nikolaos D. Grivas (2012). Υπολογισμός ισοχρονικών καμπύλων χρονοαπόστασης σε οδικά δίκτυα (Isochrone computation on road networks).


  1. Hans-Peter Kriegel, Peer Kröger, Jörg Sander, and Arthur Zimek (2011). Density-based clustering. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 1(3), 231-240, 10.1002/widm.30, BibTeX
  2. Thomas Bernecker, Michael E. Houle, Hans-Peter Kriegel, Peer Kröger, Matthias Renz, Erich Schubert, and Arthur Zimek (2011). Quality of Similarity Rankings in Time Series. SSTD, 422-440, Springer, 10.1007/978-3-642-22922-0_25, BibTeX
  3. Elke Achtert, Ahmed Hettab, Hans-Peter Kriegel, Erich Schubert, and Arthur Zimek (2011). Spatial Outlier Detection: Data, Algorithms, Visualizations. SSTD, 512-516, Springer, 10.1007/978-3-642-22922-0_41, BibTeX
  4. Yong Shi, and Li Zhang (2011). COID: A cluster-outlier iterative detection approach to multi-dimensional data analysis. Knowl. Inf. Syst. 28(3), 709-733, 10.1007/s10115-010-0323-y, BibTeX
  5. Kai Ming Ting, Takashi Washio, Jonathan R. Wells, and Fei Tony Liu (2011). Density Estimation Based on Mass. ICDM, 715-724, IEEE, 10.1109/ICDM.2011.47, BibTeX
  6. Hans-Peter Kriegel, Peer Kröger, Erich Schubert, and Arthur Zimek (2011). Interpreting and Unifying Outlier Scores. SDM, 13-24, SIAM / Omnipress, 10.1137/1.9781611972818.2, BibTeX
  7. Claudia Plant (2011). SONAR: Signal De-mixing for Robust Correlation Clustering. SDM, 319-330, SIAM / Omnipress, 10.1137/1.9781611972818.28, BibTeX
  8. Anca Maria Ivanescu, Thivaharan Albin, Dirk Abel, and Thomas Seidl (2011). Employing correlation clustering for the identification of piecewise affine models. Proceedings of the 2011 workshop on Knowledge discovery, modeling and simulation - KDMS ‘11, ACM Press, 10.1145/2023568.2023575
  9. Stephan Günnemann, Hardy Kremer, and Thomas Seidl (2011). An extension of the PMML standard to subspace clustering models. PMML ‘11, 48-53, ACM, 10.1145/2023598.2023605
  10. Resat Selbas, Arzu Sencan, and Ecir U. (2011). Data Mining Method For Energy System Aplications. Knowledge-Oriented Applications in Data Mining, InTech, 10.5772/13710
  11. Emmanuel Müller, Ira Assent, Stephan Günnemann, Patrick Gerwert, Matthias Hannen, Timm Jansen, and Thomas Seidl (2011). A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. BTW, 347-366, GI, BibTeX
  12. Hans-Peter Kriegel, Erich Schubert, and Arthur Zimek (2011). Evaluation of Multiple Clustering Solutions. MultiClust@ECML/PKDD, 55-66,, BibTeX
  13. Bilkis Jamal Ferdosi (2011). Scalable analysis and visualization of high-dimensional astronomical data sets. s.n. 9789036749633
  14. Iliya Mitov (2011). Class association rule mining using multidimensional numbered information spaces.
  15. Johan Mazel (2011). Unsupervised network anomaly detection. INSA de Toulouse
  16. Ευλάμπιος Αποστολίδης (2011). Συγκριτική μελέτη μεθόδων κατασκευής του R* TREE με όρους αποδοτικότητας για ερωτήματα κοντινότερου γείτονα σε πολυδιάστατους χώρους δεδομένων. Πανεπιστήμιο Μακεδονίας Οικονομικών και Κοινωνικών Επιστημών


  1. Elke Achtert, Hans-Peter Kriegel, Lisa Reichert, Erich Schubert, Remigius Wojdanowski, and Arthur Zimek (2010). Visual Evaluation of Outlier Detection Models. DASFAA (2), 396-399, Springer, 10.1007/978-3-642-12098-5_34, BibTeX
  2. Dominik Benz, Andreas Hotho, Robert Jäschke, Beate Krause, Folke Mitzlaff, Christoph Schmitz, and Gerd Stumme (2010). The social bookmark and publication management system bibsonomy - A platform for evaluating and demonstrating Web 2.0 research. VLDB J. 19(6), 849-875, 10.1007/s00778-010-0208-4, BibTeX
  3. Arik Messerman, Tarik Mustafic, Seyit Ahmet Çamtepe, and Sahin Albayrak (2010). A generic framework and runtime environment for development and evaluation of behavioral biometrics solutions. ISDA, 136-141, IEEE, 10.1109/ISDA.2010.5687276, BibTeX
  4. Bilkis J. Ferdosi, Hugo Buddelmeijer, Scott C. Trager, Michael H. F. Wilkinson, and Jos B. T. M. Roerdink (2010). Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological operators. IEEE VAST, 35-42, IEEE, 10.1109/VAST.2010.5652450, BibTeX
  5. Tobias Emrich, Hans-Peter Kriegel, Peer Kröger, Matthias Renz, and Andreas Züfle (2010). Boosting spatial pruning: on optimal pruning of MBRs. SIGMOD Conference, 39-50, ACM, 10.1145/1807167.1807174, BibTeX
  6. Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, and James Swee Chuan Tan (2010). Mass estimation and its applications. KDD, 989-998, ACM, 10.1145/1835804.1835929, BibTeX
  7. Tobias Emrich, Franz Graf, Hans-Peter Kriegel, Matthias Schubert, and Marisa Thoma (2010). On the impact of flash SSDs on spatial indexing. DaMoN, 3-8, ACM, 10.1145/1869389.1869390, BibTeX
  8. Albert Hein, and Thomas Kirste (2010). Unsupervised detection of motion primitives in very high dimensional sensor data. BMI, 22-37,


  1. Hans-Peter Kriegel, Peer Kröger, Erich Schubert, and Arthur Zimek (2009). Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. PAKDD, 831-838, Springer, 10.1007/978-3-642-01307-2_86, BibTeX
  2. Elke Achtert, Thomas Bernecker, Hans-Peter Kriegel, Erich Schubert, and Arthur Zimek (2009). ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series. SSTD, 436-440, Springer, 10.1007/978-3-642-02982-0_35, BibTeX
  3. Gabriela Moise, Arthur Zimek, Peer Kröger, Hans-Peter Kriegel, and Jörg Sander (2009). Subspace and projected clustering: experimental evaluation and analysis. Knowl. Inf. Syst. 21(3), 299-326, 10.1007/s10115-009-0226-y, BibTeX
  4. Hans-Peter Kriegel, Peer Kröger, and Arthur Zimek (2009). Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. TKDD 3(1), 1:1-1:58, 10.1145/1497577.1497578, BibTeX
  5. Hans-Peter Kriegel, Peer Kröger, Erich Schubert, and Arthur Zimek (2009). LoOP: local outlier probabilities. CIKM, 1649-1652, ACM, 10.1145/1645953.1646195, BibTeX
  6. Arthur Zimek (2009). Correlation clustering. SIGKDD Explorations 11(1), 53-54, 10.1145/1656274.1656286, BibTeX


  1. Elke Achtert, Hans-Peter Kriegel, and Arthur Zimek (2008). ELKI: A Software System for Evaluation of Subspace Clustering Algorithms. SSDBM, 580-585, Springer, 10.1007/978-3-540-69497-7_41, BibTeX

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