| Widening: Using Parallel Resources To Improve Model Quality | 0 | 0.34 | 2021 |
| Imprecise continuous-time Markov chains. | 5 | 0.54 | 2017 |
| Efficiently Discovering Unexpected Pattern-Co-Occurrences. | 0 | 0.34 | 2017 |
| Efficient Computation of Updated Lower Expectations for Imprecise Continuous-Time Hidden Markov Chains. | 1 | 0.38 | 2017 |
| Sharing Data with Guaranteed Privacy. | 0 | 0.34 | 2016 |
| Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences | 1 | 0.35 | 2015 |
| Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns | 7 | 0.49 | 2015 |
| Characterising Seismic Data. | 1 | 0.36 | 2014 |
| MDL in Pattern Mining A Brief Introduction to Krimp. | 1 | 0.36 | 2014 |
| Queries for data analysis | 0 | 0.34 | 2012 |
| Smoothing categorical data | 1 | 0.36 | 2012 |
| Krimp: mining itemsets that compress | 119 | 3.20 | 2011 |
| A Structure Function for Transaction Data. | 3 | 0.43 | 2011 |
| Frequent episode mining to support pattern analysis in developmental biology | 0 | 0.34 | 2010 |
| Learning predictive models that use pattern discovery--a bootstrap evaluative approach applied in organ functioning sequences. | 6 | 0.86 | 2010 |
| Low-Entropy Set Selection | 10 | 0.67 | 2009 |
| Mining Databases to Mine Queries Faster | 1 | 0.38 | 2009 |
| Identifying the Components | 7 | 0.54 | 2009 |
| Compressing tags to find interesting media groups | 9 | 0.67 | 2009 |
| Advances in Intelligent Data Analysis VIII, 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009. Proceedings | 33 | 2.95 | 2009 |
| Characteristic relational patterns | 5 | 0.45 | 2009 |
| StreamKrimp: Detecting Change in Data Streams | 29 | 1.36 | 2008 |
| Discovering Relational Items Sets Efficiently | 11 | 0.65 | 2008 |
| Preserving Privacy through Data Generation | 0 | 0.34 | 2007 |
| Finding Composite Episodes | 0 | 0.34 | 2007 |
| Characterising the difference | 21 | 1.12 | 2007 |
| 07181 Introduction -- Parallel Universes and Local Patterns | 0 | 0.34 | 2007 |
| Understanding Discrete Classifiers with a Case Study in Gene Prediction | 1 | 0.39 | 2007 |
| 07181 Abstracts Collection -- Parallel Universes and Local Patterns | 0 | 0.34 | 2007 |
| Parallel Universes and Local Patterns, 01.05. - 04.05.2007 | 8 | 1.97 | 2007 |
| Compression picks item sets that matter | 25 | 1.49 | 2006 |
| Item Sets that Compress | 87 | 3.83 | 2006 |
| Workshop Organizing Committees | 0 | 0.34 | 2006 |
| Combination of text-mining algorithms increases the performance. | 12 | 0.80 | 2006 |
| Reducing the Frequent Pattern Set | 8 | 0.68 | 2006 |
| Instability of Classifiers on Categorical Data | 1 | 0.42 | 2005 |
| Data mining in inductive databases | 5 | 0.48 | 2005 |
| CONAN: an integrative system for biomedical literature mining | 2 | 0.42 | 2005 |
| Local Pattern Detection, International Seminar, Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers | 14 | 0.91 | 2005 |
| Advances in Intelligent Data Analysis VI, 6th International Symposium on Intelligent Data Analysis, IDA 2005, Madrid, Spain, September 8-10, 2005, Proceedings | 43 | 4.33 | 2005 |
| KDID 2004, Knowledge Discovery in Inductive Databases, Proceedings of the Third International Workshop on Knowledge Discovery inInductive Databases, Pisa, Italy, September 20, 2004, Revised Selected and Invited Papers | 8 | 0.59 | 2005 |
| Discovery of regulatory connections in microarray data | 1 | 0.37 | 2004 |
| 04161 Abstracts Collection - Detecting Local Patterns. | 0 | 0.34 | 2004 |
| Constructing (Almost) phylogenetic trees from developmental sequences data | 6 | 0.69 | 2004 |
| Involving Aggregate Functions in Multi-relational Search | 24 | 1.23 | 2002 |
| Complex Data: Mining Using Patterns | 3 | 0.39 | 2002 |
| Propositionalisation and Aggregates | 45 | 2.90 | 2001 |
| MAMBO: Discovering Association Rules Based on Conditional Independencies | 8 | 0.67 | 2001 |
| Principles of Data Mining and Knowledge Discovery, 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001, Proceedings | 40 | 7.71 | 2001 |
| Multi-Relational Data Mining, Using UML for ILP | 8 | 0.76 | 2000 |
