| Global optimality in k-means clustering. | 2 | 0.36 | 2018 |
| Quantitative Redundancy in Partial Implications. | 0 | 0.34 | 2015 |
| Learning Propositional Horn Formulas from Closure Queries. | 0 | 0.34 | 2015 |
| Evaluation of Association Rule Quality Measures through Feature Extraction. | 1 | 0.35 | 2013 |
| Formal and computational properties of the confidence boost of association rules | 5 | 0.43 | 2011 |
| Parameter-free Association Rule Mining with Yacaree | 3 | 0.57 | 2011 |
| Construction and learnability of canonical Horn formulas | 15 | 0.88 | 2011 |
| Border algorithms for computing hasse diagrams of arbitrary lattices | 2 | 0.44 | 2011 |
| Towards Parameter-free Data Mining: Mining Educational Data with Yacaree. | 3 | 0.49 | 2011 |
| Mining frequent closed rooted trees | 9 | 0.56 | 2010 |
| Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part I | 69 | 4.36 | 2010 |
| Redundancy, Deduction Schemes, and Minimum-Size Bases for Association Rules | 7 | 0.63 | 2010 |
| Objective Novelty of Association Rules : Measuring the Confidence Boost | 2 | 0.39 | 2010 |
| Closed-set-based Discovery of Representative Association Rules Revisited | 1 | 0.35 | 2010 |
| Special issue for ECML PKDD 2010: Guest editors' introduction | 0 | 0.34 | 2010 |
| Filtering Association Rules with Negations on the Basis of Their Confidence Boost. | 3 | 0.40 | 2010 |
| Closure-Based Confidence Boost in Association Rules | 2 | 0.36 | 2010 |
| Canonical horn representations and query learning | 7 | 0.60 | 2009 |
| Deduction Schemes for Association Rules | 6 | 0.46 | 2008 |
| Provably Fast Training Algorithms for Support Vector Machines | 21 | 1.55 | 2008 |
| Minimum-Size Bases of Association Rules | 6 | 0.51 | 2008 |
| Mining Implications from Lattices of Closed Trees | 5 | 0.48 | 2008 |
| Characterizing Implications of Injective Partial Orders | 1 | 0.38 | 2007 |
| Mining Frequent Closed Unordered Trees Through Natural Representations | 10 | 0.60 | 2007 |
| Horn axiomatizations for sequential data | 10 | 0.62 | 2007 |
| A general dimension for query learning | 9 | 0.64 | 2007 |
| Algorithmic Learning Theory, 17th International Conference, ALT 2006, Barcelona, Spain, October 7-10, 2006, Proceedings | 25 | 1.63 | 2006 |
| The consistency dimension and distribution-dependent learning from queries | 12 | 1.12 | 2002 |
| A Best-First Strategy for Finding Frequent Sets | 0 | 0.34 | 2002 |
| A New Abstract Combinatorial Dimension for Exact Learning via Queries | 13 | 0.82 | 2002 |
| Bounding Negative Information in Frequent Sets Algorithms | 4 | 0.39 | 2001 |
| A Random Sampling Technique for Training Support Vector Machines | 34 | 2.03 | 2001 |
| A General Dimension for Exact Learning | 6 | 0.60 | 2001 |
| Abstract Combinatorial Characterizations of Exact Learning via Queries | 5 | 0.57 | 2000 |
| The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract) | 6 | 0.68 | 1999 |
| The Consistency Dimension, Compactness, and Query Learning | 1 | 0.36 | 1999 |
| The structure of logarithmic advice complexity classes | 6 | 0.76 | 1998 |
| Coding Complexity: The Computational Complexity of Succinct Descriptions | 1 | 0.35 | 1997 |
| Algorithms for Learning Finite Automata from Queries: A Unified View | 32 | 1.50 | 1997 |
| The complexity of searching implicit graphs | 17 | 0.93 | 1996 |
| An optimal parallel algorithm for learning DFA | 8 | 0.63 | 1996 |
| The Complexity of Searching Succinctly Represented Graphs | 1 | 0.35 | 1995 |
| Adaptive Logspace Reducibility and Parallel Time | 7 | 0.79 | 1995 |
| Learnability of Kolmogorov-easy circuit expressions via queries | 0 | 0.34 | 1995 |
| Simple PAC Learning of Simple Decision Lists | 8 | 0.69 | 1995 |
| The Query Complexity of Learning DFA | 6 | 0.58 | 1994 |
| Some Structural Complexity Aspects of Neural Computation | 11 | 0.99 | 1993 |
| Characterizations of Logarithmic Advice Complexity Classes | 13 | 1.02 | 1992 |
| A Note on the Query Complexity of Learning DFA (Extended Abstract) | 2 | 0.41 | 1992 |
| Deciding bisimilarity is P-complete | 45 | 2.41 | 1992 |
