On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study GO Campos, A Zimek, J Sander, RJGB Campello, B Micenková, ... Data mining and knowledge discovery 30 (4), 891-927, 2016 | 539 | 2016 |
Evaluating clustering in subspace projections of high dimensional data E Müller, S Günnemann, I Assent, T Seidl Proceedings of the VLDB Endowment 2 (1), 1270-1281, 2009 | 339 | 2009 |
The clustree: indexing micro-clusters for anytime stream mining P Kranen, I Assent, C Baldauf, T Seidl Knowledge and information systems 29 (2), 249-272, 2011 | 296 | 2011 |
DUSC: Dimensionality unbiased subspace clustering I Assent, R Krieger, E Müller, T Seidl seventh IEEE international conference on data mining (ICDM 2007), 409-414, 2007 | 158 | 2007 |
Clicks: An effective algorithm for mining subspace clusters in categorical datasets MJ Zaki, M Peters, I Assent, T Seidl Data & Knowledge Engineering 60 (1), 51-70, 2007 | 138 | 2007 |
Clustering high dimensional data I Assent Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2 (4 …, 2012 | 126 | 2012 |
INSCY: Indexing subspace clusters with in-process-removal of redundancy I Assent, R Krieger, E Müller, T Seidl 2008 Eighth IEEE International Conference on Data Mining, 719-724, 2008 | 123 | 2008 |
Explaining outliers by subspace separability B Micenková, RT Ng, XH Dang, I Assent 2013 IEEE 13th international conference on data mining, 518-527, 2013 | 117 | 2013 |
Anyout: Anytime outlier detection on streaming data I Assent, P Kranen, C Baldauf, T Seidl International Conference on Database Systems for Advanced Applications, 228-242, 2012 | 116 | 2012 |
The TS-tree: efficient time series search and retrieval I Assent, R Krieger, F Afschari, T Seidl Proceedings of the 11th international conference on Extending database …, 2008 | 99 | 2008 |
OutRank: ranking outliers in high dimensional data E Muller, I Assent, U Steinhausen, T Seidl 2008 IEEE 24th international conference on data engineering workshop, 600-603, 2008 | 94 | 2008 |
Outlier ranking via subspace analysis in multiple views of the data E Müller, I Assent, P Iglesias, Y Mülle, K Böhm 2012 IEEE 12th international conference on data mining, 529-538, 2012 | 90 | 2012 |
Relevant subspace clustering: Mining the most interesting non-redundant concepts in high dimensional data E Müller, I Assent, S Günnemann, R Krieger, T Seidl 2009 Ninth IEEE International Conference on Data Mining, 377-386, 2009 | 90 | 2009 |
Outsourced similarity search on metric data assets ML Yiu, I Assent, CS Jensen, P Kalnis IEEE Transactions on knowledge and data engineering 24 (2), 338-352, 2010 | 89 | 2010 |
Discriminative features for identifying and interpreting outliers XH Dang, I Assent, RT Ng, A Zimek, E Schubert 2014 IEEE 30th international conference on data engineering, 88-99, 2014 | 87 | 2014 |
Approximation techniques for indexing the earth mover's distance in multimedia databases I Assent, A Wenning, T Seidl 22nd International Conference on Data Engineering (ICDE'06), 11-11, 2006 | 87 | 2006 |
Self-adaptive anytime stream clustering P Kranen, I Assent, C Baldauf, T Seidl 2009 Ninth IEEE International Conference on Data Mining, 249-258, 2009 | 85 | 2009 |
Anticipatory DTW for efficient similarity search in time series databases I Assent, M Wichterich, R Krieger, H Kremer, T Seidl Proceedings of the VLDB Endowment 2 (1), 826-837, 2009 | 81 | 2009 |
VISA: visual subspace clustering analysis I Assent, R Krieger, E Müller, T Seidl ACM SIGKDD Explorations Newsletter 9 (2), 5-12, 2007 | 74 | 2007 |
Efficient emd-based similarity search in multimedia databases via flexible dimensionality reduction M Wichterich, I Assent, P Kranen, T Seidl Proceedings of the 2008 ACM SIGMOD International Conference on Management of …, 2008 | 72 | 2008 |