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Note: Links lead to the DBLP on the Web. Francesco Bonchi Francesco Bonchi, Jean-François Boulicaut : Knowledge Discovery in Inductive Databases, 4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers Springer 2006 Francesco Bonchi, Fosca Giannotti , Claudio Lucchese , Salvatore Orlando , Raffaele Perego , Roberto Trasarti : ConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery. ICDE 2006 : 159 Francesco Bonchi, Claudio Lucchese : On condensed representations of constrained frequent patterns. Knowl. Inf. Syst. 9 (2): 180-201 (2006) Maurizio Atzori , Francesco Bonchi, Fosca Giannotti , Dino Pedreschi : Blocking Anonymity Threats Raised by Frequent Itemset Mining. ICDM 2005 : 561-564 Francesco Bonchi, Claudio Lucchese : Pushing Tougher Constraints in Frequent Pattern Mining. PAKDD 2005 : 114-124 Maurizio Atzori , Francesco Bonchi, Fosca Giannotti , Dino Pedreschi : k -Anonymous Patterns. PKDD 2005 : 10-21 Stefano Bistarelli , Francesco Bonchi: Interestingness is Not a Dichotomy: Introducing Softness in Constrained Pattern Mining. PKDD 2005 : 22-33 Francesco Bonchi, Fosca Giannotti , Alessio Mazzanti , Dino Pedreschi : Exante: A Preprocessing Method for Frequent-Pattern Mining. IEEE Intelligent Systems 20 (3): 25-31 (2005) Francesco Bonchi, Fosca Giannotti , Alessio Mazzanti , Dino Pedreschi : Efficient breadth-first mining of frequent pattern with monotone constraints. Knowl. Inf. Syst. 8 (2): 131-153 (2005) Francesco Bonchi, Fosca Giannotti , Dino Pedreschi : A Relational Query Primitive for Constraint-Based Pattern Mining. Constraint-Based Mining and Inductive Databases 2004 : 14-37 Francesco Bonchi, Claudio Lucchese : On Closed Constrained Frequent Pattern Mining. ICDM 2004 : 35-42 Francesco Bonchi, Fosca Giannotti : Pushing Constraints to Detect Local Patterns. Local Pattern Detection 2004 : 1-19 Francesco Bonchi, Bart Goethals : FP-Bonsai: The Art of Growing and Pruning Small FP-Trees. PAKDD 2004 : 155-160 Francesco Bonchi, Fosca Giannotti , Dino Pedreschi : Frequent Pattern Queries for Flexible Knowledge Discovery. SEBD 2004 : 250-261 Francesco Bonchi, Fosca Giannotti , Alessio Mazzanti , Dino Pedreschi : ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraint. ICDM 2003 : 11-18 Francesco Bonchi, Fosca Giannotti , Alessio Mazzanti , Dino Pedreschi : Adaptive Constraint Pushing in Frequent Pattern Mining. PKDD 2003 : 47-58 Francesco Bonchi, Fosca Giannotti , Alessio Mazzanti , Dino Pedreschi : ExAnte: Anticipated Data Reduction in Constrained Pattern Mining. PKDD 2003 : 59-70 Francesco Bonchi, Fosca Giannotti , Alessio Mazzanti , Dino Pedreschi : Pre-processing for Constrained Pattern Mining. SEBD 2003 : 519-530 Francesco Bonchi, Fosca Giannotti , Giuseppe Manco , Chiara Renso , Mirco Nanni , Dino Pedreschi , Salvatore Ruggieri : Data Mining for Intelligent Web Caching. ITCC 2001 : 599-603 Francesco Bonchi, Fosca Giannotti , Cristian Gozzi , Giuseppe Manco , Mirco Nanni , Dino Pedreschi , Chiara Renso , Salvatore Ruggieri : Web log data warehousing and mining for intelligent web caching. Data Knowl. Eng. 39 (2): 165-189 (2001) Francesco Bonchi, Fosca Giannotti , Dino Pedreschi : On Verification in Logic Database Languages. Computational Logic 2000 : 957-971 Francesco Bonchi, Fosca Giannotti , Gianni Mainetto , Dino Pedreschi : Using Data Mining Techniques in Fiscal Fraud Detection. DaWaK 1999 : 369-376 Francesco Bonchi, Fosca Giannotti , Gianni Mainetto , Dino Pedreschi : A Classification-Based Methodology for Planning Audit Strategies in Fraud Detection. KDD 1999 : 175-184 Francesco Bonchi, Fosca Giannotti , Gianni Mainetto , Dino Pedreschi : Una Metodologia Basata sulla Classificazione per la Pianificazione degli Accertamenti nel Rilevamento di Frodi. SEBD 1999 : 69-84 1 [ 19 ] [ 21 ] 2 [ 18 ] 3 [ 24 ] 4 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] [ 10 ] [ 11 ] [ 13 ] [ 15 ] [ 16 ] [ 17 ] [ 19 ] [ 21 ] [ 23 ] 5 [ 12 ] 6 [ 5 ] 7 [ 14 ] [ 20 ] [ 22 ] [ 23 ] 8 [ 1 ] [ 2 ] [ 3 ] 9 [ 5 ] [ 6 ] 10 [ 7 ] [ 8 ] [ 9 ] [ 10 ] [ 16 ] [ 17 ] 11 [ 5 ] [ 6 ] 12 [ 23 ] 13 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] [ 9 ] [ 10 ] [ 11 ] [ 15 ] [ 16 ] [ 17 ] [ 19 ] [ 21 ] 14 [ 23 ] 15 [ 5 ] [ 6 ] 16 [ 5 ] [ 6 ] 17 [ 23 ]  ©2006 Association for Computing Machinery |
