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Note: Links lead to the DBLP on the Web. Bart Goethals Bart Goethals, Arno Siebes : 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 Springer 2005 Bart Goethals, Eveline Hoekx , Jan Van den Bussche : Mining Tree Queries in a Graph. BNAIC 2005 : 345-346 Bart Goethals, Eveline Hoekx , Jan Van den Bussche : Mining tree queries in a graph. KDD 2005 : 61-69 Toon Calders , Bart Goethals: Quick Inclusion-Exclusion. KDID 2005 : 86-103 Toon Calders , Bart Goethals: Depth-First Non-Derivable Itemset Mining. SDM 2005 Bart Goethals, Juho Muhonen , Hannu Toivonen : Mining Non-Derivable Association Rules. SDM 2005 Bart Goethals: Frequent Set Mining. The Data Mining and Knowledge Discovery Handbook 2005 : 377-397 Floris Geerts , Bart Goethals, Jan Van den Bussche : Tight upper bounds on the number of candidate patterns. ACM Trans. Database Syst. 30 (2): 333-363 (2005) Bart Goethals, Siegfried Nijssen , Mohammed Javeed Zaki : Open source data mining: workshop report. SIGKDD Explorations 7 (2): 143-144 (2005) Roberto J. Bayardo Jr. , Bart Goethals, Mohammed Javeed Zaki : FIMI '04, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Brighton, UK, November 1, 2004 CEUR-WS.org 2004 Floris Geerts , Bart Goethals, Taneli Mielikäinen : Tiling Databases. Discovery Science 2004 : 278-289 Bart Goethals, Sven Laur , Helger Lipmaa , Taneli Mielikäinen : On Private Scalar Product Computation for Privacy-Preserving Data Mining. ICISC 2004 : 104-120 Francesco Bonchi , Bart Goethals: FP-Bonsai: The Art of Growing and Pruning Small FP-Trees. PAKDD 2004 : 155-160 Bart Goethals: Memory issues in frequent itemset mining. SAC 2004 : 530-534 Bart Goethals, Mohammed Javeed Zaki : Advances in frequent itemset mining implementations: report on FIMI'03. SIGKDD Explorations 6 (1): 109-117 (2004) Bart Goethals, Mohammed Javeed Zaki : FIMI '03, Frequent Itemset Mining Implementations, Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, 19 December 2003, Melbourne, Florida, USA CEUR-WS.org 2003 Bart Goethals, Mohammed Javeed Zaki : Advances in Frequent Itemset Mining Implementations: Introduction to FIMI03. FIMI 2003 Floris Geerts , Bart Goethals, Taneli Mielikäinen : What You Store is What You Get. KDID 2003 : 60-69 Toon Calders , Bart Goethals: Minimal k -Free Representations of Frequent Sets. PKDD 2003 : 71-82 Toon Calders , Bart Goethals: Mining All Non-derivable Frequent Itemsets. PKDD 2002 : 74-85 Bart Goethals, Jan Van den Bussche : Relational Association Rules: Getting WARMeR. Pattern Detection and Discovery 2002 : 125-139 Toon Calders , Bart Goethals: Mining All Non-Derivable Frequent Itemsets CoRR cs.DB/0206004 : (2002) Bart Goethals, Jan Van den Bussche : Relational Association Rules: getting WARMeR CoRR cs.DB/0206023 : (2002) Floris Geerts , Bart Goethals, Jan Van den Bussche : A Tight Upper Bound on the Number of Candidate Patterns. ICDM 2001 : 155-162 Floris Geerts , Bart Goethals, Jan Van den Bussche : A Tight Upper Bound on the Number of Candidate Patterns CoRR cs.DB/0112007 : (2001) Bart Goethals, Jan Van den Bussche : Interactive Constrained Association Rule Mining CoRR cs.DB/0112011 : (2001) Tom Brijs , Bart Goethals, Gilbert Swinnen , Koen Vanhoof , Geert Wets : A Data Mining Framework for Optimal Product Selection in Retail Supermarket Data: The Generalized PROFSET Model CoRR cs.DB/0112013 : (2001) Bart Goethals, Jan Van den Bussche : On Supporting Interactive Association Rule Mining. DaWaK 2000 : 307-316 Tom Brijs , Bart Goethals, Gilbert Swinnen , Koen Vanhoof , Geert Wets : A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model. KDD 2000 : 300-304 Bart Goethals, Jan Van den Bussche : A priori versus a posteriori filtering of association rules. 1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery 1999 1 [ 21 ] 2 [ 18 ] 3 [ 2 ] [ 4 ] 4 [ 1 ] [ 3 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 10 ] [ 23 ] [ 28 ] [ 29 ] 5 [ 9 ] [ 11 ] [ 12 ] [ 26 ] [ 27 ] 6 [ 6 ] [ 7 ] [ 13 ] [ 20 ] [ 23 ] 7 [ 28 ] [ 29 ] 8 [ 19 ] 9 [ 19 ] 10 [ 13 ] [ 19 ] [ 20 ] 11 [ 25 ] 12 [ 22 ] 13 [ 30 ] 14 [ 2 ] [ 4 ] 15 [ 25 ] 16 [ 2 ] [ 4 ] 17 [ 2 ] [ 4 ] 18 [ 14 ] [ 15 ] [ 16 ] [ 21 ] [ 22 ]  ©2006 Association for Computing Machinery |
