Gregory Piatetsky-Shapiro Papers on DiSC'04 Capturing best practice for microarray gene expression data analysis Summary from the KDD-03 Panel -- Data Mining: The Next 10 Years Microarray Data Mining: Facing the Challenges Publications Note: Links lead to the DBLP on the Web. Gregory Piatetsky-Shapiro Gregory Piatetsky-Shapiro, Tom Khabaza , Sridhar Ramaswamy : Capturing best practice for microarray gene expression data analysis. KDD 2003 : 407-415 Xindong Wu , Philip S. Yu , Gregory Piatetsky-Shapiro: Data Mining: How Research Meets Practical Development? Knowl. Inf. Syst 5 (2): 248-261 (2003) Sam Steingold , Richard Wherry , Gregory Piatetsky-Shapiro: Measuring Real-Time Predictive Models. ICDM 2001 : 649-650 Steve Gallant , Gregory Piatetsky-Shapiro, Ming Tan : Value-based data mining and web mining for CRM. KDD Tutorials 2001 Gregory Piatetsky-Shapiro: KDnuggets Interview with Usama Fayyad. SIGKDD Explorations 3 (1): 45-48 (2001) Gregory Piatetsky-Shapiro: KDnuggets Interview with Jesus Mena. SIGKDD Explorations 3 (1): 49-50 (2001) Gregory Piatetsky-Shapiro: Knowledge Discovery in Databases: Ten years after. SIGKDD Explorations 1 (2): 59-61 (2000) Gregory Piatetsky-Shapiro, Sam Steingold : Measuring Lift Quality in Database Marketing. SIGKDD Explorations 2 (2): 76-80 (2000) Gregory Piatetsky-Shapiro, Brij M. Masand : Estimating Campaign Benefits and Modeling Lift. KDD 1999 : 185-193 Gregory Piatetsky-Shapiro: Data Mining and Knowledge Discovery: The Third Generation (Extended Abstract). ISMIS 1997 : 48-49 Usama M. Fayyad , Gregory Piatetsky-Shapiro, Padhraic Smyth , Ramasamy Uthurusamy : Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press 1996 Gregory Piatetsky-Shapiro, Graham J. Wills : Information Exploration Shootout. IEEE Visualization 1996 : 449-450 Gregory Piatetsky-Shapiro: Data Mining and Knowledge Discovery in Business Databases. ISMIS 1996 : 56-67 Brij M. Masand , Gregory Piatetsky-Shapiro: A Comparison of Approaches for Maximizing Business Payoff of Prediction Models. KDD 1996 : 195-201 Usama M. Fayyad , Gregory Piatetsky-Shapiro, Padhraic Smyth : Knowledge Discovery and Data Mining: Towards a Unifying Framework. KDD 1996 : 82-88 Gregory Piatetsky-Shapiro, Ronald J. Brachman , Tom Khabaza , Willi Klösgen , Evangelos Simoudis : An Overview of Issues in Developing Industrial Data Mining and Knowledge Discovery Applications. KDD 1996 : 89-95 Usama M. Fayyad , Gregory Piatetsky-Shapiro, Padhraic Smyth : From Data Mining to Knowledge Discovery: An Overview. Advances in Knowledge Discovery and Data Mining 1996 : 1-34 Christopher J. Matheus , Gregory Piatetsky-Shapiro, Dwight McNeill : Selecting and reporting What Is Interesting. Advances in Knowledge Discovery and Data Mining 1996 : 495-515 Gregory Piatetsky-Shapiro: Data Mining and Knowledge Discovery Internet Resources. Advances in Knowledge Discovery and Data Mining 1996 : 593-595 Gregory Piatetsky-Shapiro: Knowledge Discovery and Acquisition From Imperfect Information. Uncertainty Management in Information Systems 1996 : 155-188 Usama M. Fayyad , Gregory Piatetsky-Shapiro, Padhraic Smyth : From Data Mining to Knowledge Discovery in Databases. AI Magazine 17 (3): 37-54 (1996) Usama M. Fayyad , Gregory Piatetsky-Shapiro, Padhraic Smyth : The KDD Process for Extracting Useful Knowledge from Volumes of Data. Commun. ACM 39 (11): 27-34 (1996) Ronald J. Brachman , Tom Khabaza , Willi Klösgen , Gregory Piatetsky-Shapiro, Evangelos Simoudis : Mining Business Databases. Commun. ACM 39 (11): 42-48 (1996) Gregory Piatetsky-Shapiro: Knowledge Discovery in Personal Data vs. Privacy: A mini-symposium. IEEE Expert 10 (2): 46-47 (1995) Gregory Piatetsky-Shapiro: Guest Editor's Introduction: Knowledge Discovery in Databases-from Research to Applications. J. Intell. Inf. Syst. 4 (1): 5-6 (1995) Gregory Piatetsky-Shapiro, Christopher J. Matheus : The Interingness of Deviations. KDD Workshop 1994 : 25-36 Christopher J. Matheus , Gregory Piatetsky-Shapiro, Dwight McNeill : An Application of KEFM to the Analysis of Healthcare Information. KDD Workshop 1994 : 441-452 Gregory Piatetsky-Shapiro, Christopher J. Matheus , Padhraic Smyth , Ramasamy Uthurusamy : KDD-93: Progress and Challenges in Knowledge Discovery in Databases. AI Magazine 15 (3): 77-82 (1994) Gregory Piatetsky-Shapiro: An Overview of Knowledge Discovery in Database: Recent Progress and Challenges. RSKD 1993 : 1-10 Christopher J. Matheus , Philip K. Chan , Gregory Piatetsky-Shapiro: Systems for Knowledge Discovery in Databases. IEEE Trans. Knowl. Data Eng. 5 (6): 903-913 (1993) William J. Frawley , Gregory Piatetsky-Shapiro, Christopher J. Matheus : Knowledge Discovery in Databases: An Overview. AI Magazine 13 (3): 57-70 (1992) Gregory Piatetsky-Shapiro, William J. Frawley : Knowledge Discovery in Databases AAAI/MIT Press 1991 William J. Frawley , Gregory Piatetsky-Shapiro, Christopher J. Matheus : Knowledge Discovery in Databases: An Overview. Knowledge Discovery in Databases 1991 : 1-30 Gregory Piatetsky-Shapiro: Discovery, Analysis, and Presentation of Strong Rules Knowledge Discovery in Databases 1991 : 229-248 Gregory Piatetsky-Shapiro: Knowledge Discovery in Real Databases: A Report on the IJCAI-89 Workshop. AI Magazine 11 (5): 68-70 (1991) M. Rajinikanth , Gabriel Jakobson , Carol Lafond , W. Papp , Gregory Piatetsky-Shapiro: Multiple Database Integration in Calida: Design and Implementation. ICSI 1990 : 378-384 Gabriel Jakobson , Gregory Piatetsky-Shapiro, Carol Lafond , M. Rajinikanth , J. Hernandez : CALIDA: A System for Integrated Retrieval from Multiple Heterogeneous Databases. JCDKB 1988 : 3-18 Gregory Piatetsky-Shapiro, Gabriel Jakobson : An Intermediate Database Language and its Rule-Based Transformation to Different Database Languages. Data Knowl. Eng. 2 : 1-29 (1987) Gregory Piatetsky-Shapiro, Charles Connell : Accurate Estimation of the Number of Tuples Satisfying a Condition. SIGMOD Conference 1984 : 256-276 Gregory Piatetsky-Shapiro: The Optimal Selection of Secondary Indices is NP-Complete. SIGMOD Record 13 (2): 72-75 (1983) 1 [ 18 ] [ 25 ] 2 [ 11 ] 3 [ 2 ] 4 [ 19 ] [ 20 ] [ 24 ] [ 26 ] [ 30 ] 5 [ 8 ] [ 9 ] [ 10 ] 6 [ 37 ] 7 [ 4 ] 8 [ 3 ] [ 4 ] [ 5 ] 9 [ 18 ] [ 25 ] [ 40 ] 10 [ 18 ] [ 25 ] 11 [ 4 ] [ 5 ] 12 [ 27 ] [ 32 ] 13 [ 8 ] [ 10 ] [ 11 ] [ 13 ] [ 14 ] [ 15 ] [ 23 ] 14 [ 14 ] [ 23 ] 15 [ 5 ] 16 [ 4 ] [ 5 ] 17 [ 40 ] 18 [ 18 ] [ 25 ] 19 [ 13 ] [ 19 ] [ 20 ] [ 24 ] [ 26 ] [ 30 ] 20 [ 33 ] [ 38 ] 21 [ 37 ] 22 [ 13 ] [ 30 ] 23 [ 38 ] 24 [ 29 ] 25 [ 39 ] 26 [ 39 ] ©2004 Association for Computing Machinery