Charu C. Aggarwal & Philip S. Yu "A New Framework for Itemset Generation". ACM SIGMOD PODS 97, 1997.
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1 $SpQGLFH( %LEOLRJUDItD Charu C. Aggarwal & Philip S. Yu "A New Framework for Itemset Generation". ACM SIGMOD PODS 97, Rakesh Agrawal & Kyuseok Shim "Developing tightly-coupled applications on IBM DB2/CS Relational Database System: Methodology and Experience". IBM Research Report. IBM Almaden Research Center, San Jose, California, USA, 1995 Rakesh Agrawal & Ramakrishnan Skirant "Fast Algorithms for Mining Association Rules". IBM Research Report RJ9839 IBM Almaden Research Center, San Jose, California, USA, June 1994 Rakesh Agrawal & John C. Shafer "Parallel Mining of Association Rules" IEEE Transactions on Knowledge and Data Engineering, December 1996 Kamal Ali, Stefanos Manganaris & Ramakrishnan Skirant "Partial Classification Using Association Rules" KDD 97, California (USA), 1997 Roberto J. Bayardo Jr. "Efficiently Mining Long Patterns from Databases" ACM PODS 98 [Principles of Database Systems], Seattle, Washington, USA, June 1998 Michael J. A. Berry & Gordon Linoff "Data Mining Techniques: for Marketing, Sales, and Customer Support" USA: John Wiley and Sons, 1997 "Borland Database Engine Online Help" Borland International, 1997 R.J. Brachman, T. Khabaza, W. Kloesgen, G. Piatetsky-Shapiro & E. Simoudis "Mining Business Databases" Wray Buntine & Rich Caruana "Introduction to IND and Recursive Partitioning" Research Institute for Advanced Computer Science "An Introduction to CART Methodology" California Statistical Software, Inc., 1985
2 David W. Cheung, Jiawei Han, Vincent T. Ng, Ada W. Fu & Wongjian Fu "A Fast Distributed Algorithm for Mining Association Rules" Proceedings of the International Conference on Parallel and Distributed Information Systems Florida, December 1996 David W. Cheung, Jiawei Han, Vincent T. Ng & C.Y. Wong "Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique" 1996 International Conference on Data Engineering, New Orleans, Louisiana, February 1996 David W. Cheung, Vincent T. Ng & Benjamin W. Tam "Maintenance of Discovered Knowledge: A Case in Multi-level Association Rules" 2nd International Conference on KDD, Oregon, August 1996 Peter Clark & Tim Niblett "The CN2 Induction Algorithm" Machine Learning Journal, 3 (4), pp , Netherlands: Kluwer, 1989 Peter Clark & Robin Boswell "Rule Induction with CN2: Some Recent Improvements" Machine Learning, EWSL-91 Proceedings, pp , Berlin: Springer-Verlag, 1991 A. Díaz (coordinador), F. Glover, H.M. Ghaziri, J.L. González, M. Laguna, P. Moscato, F.T. Tseng "Optimización Heurística y Redes Neuronales" Madrid: Editorial Paraninfo, Hubert L. Dreyfus "What Computers Still Can't Do. A Critique of Artificial Reason" The MIT Press, 1994 [Fourth printing] R. Duda & P. Hart "Pattern Classification and Scene Analysis" John Wiley and Sons, 1973 Oren Erzioni "The World-Wide-Web: Quagmire or Gold Mine?" Usama Fayyad, David Haussler & Paul Stolorz "Mining Scientific Data" Usama M. Fayyad & Keki B. Irani "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning". Machine Learning Usama Fayyad, Gregory Piatetsky-Shapiro & Padhraic Smyth "The KDD Process for Extracting Useful Knowledge from Volumes of Data" Usama Fayyad & Ramasamy Uthurusamy "Data Mining and Knowledge Discovery in Databases"
3 R. Feldman, A. Amir, Y. Auman, A. Zilberstien & H. Hirsh "Incremental Algorithms for Association Generation" En "KDD. Techniques and Applications", H. Lu et al. eds., World Scientific, Agustín Froufe "Tutorial de Java en Español" Sevilla, 1996 K.S. Fu, R.C. González & C.S.G. Lee "Robótica. Control, Detección, Visión e Inteligencia" McGraw-Hill, 1990 Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita & Takeshi Tokuyama: "Mining Optimized Association Rules for Numeric Attributes" PODS'96, Montreal, Quebec, Canadá, 1996 Robert L. Glass "Everything old is new again" Practical Programmer Communications of the ACM, May 1998 Clark Glymour, David Madigan, Daryl Pregibon & Padhraic Smyth "Statistical Inference and Data Mining" Graham Hamilton & Rick Cattell "JDBC TM : A Java SQL" Version 1.20, February 9, 1998 Jiawei Han & Yongjian Fu "Discovery of multiple-level association rules from large databases" 21st Intl. Conference on Very Large Databases, Zürich, Jia Liang Han & Ashley W. Plank "Background for Association Rules and Cost Estimate of Selected Mining Algorithms" ACM CIKM'96 (International Conference on Information and Knowledge Management) Rockville, Maryland, USA, 1996 Tomasz Imielinski & Heikki Mannila "A Database Perspective on Knowledge Discovery" W. H. Inmon "The Data Warehouse and Data Mining" M.A. Ismael & M.S. Kamel "Multidimensional Data Clustering Utilizing Hybrid Search Strategies" Pattern Recognition 22, 1989, pgs A.K. Jain & R.C. Dubes "Algorithms for Clustering Data" Prentice Hall, 1988.
4 "JDK 1.x.x Documentation" JavaSoft R.W. Klein & R.C. Dubes "Experiments in Projection and Clustering by Simulated Annealing" Pattern Recognition 22, 1989, pgs M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen & A.I. Verkamo "Finding Interesting Rules from Large Sets of Discovered Association Rules" ACM CIKM 94, Gaitherburg, Maryland, USA, November 1994 Arno J. Knobbe, Pieter W. Adriaans "Analysing Binary Associations" 2nd International Conference on KDD, Oregon, August 1996 Yves Kodratoff "Introduction to Machine Learning" Pitman Publishing, 1988 Laura Lemay & Charles L. Perkins "Teach yourself JAVA in 21 days" USA: Sams.net Publishing, [1ª edición]. Brian Lent, Arun Swami & Jennifer Widom "Clustering Association Rules" Stanford University Scott M. Lewandoswski "Frameworks for Component-Based Client/Server Computing" ACM Computing Surveys, March 1998 Heikki Mannila, Hannu Toivonen & A. Inkeri Verkamo "Improved Methods for Finding Association Rules" Department of Computer Science, University of Helsinki, C , Helsinki, December 1993 Heikki Mannila, Hannu Toivonen & A. Inkeri Verkamo "Discovering frecuent episodes in sequences" KDD'95 Nimrod Megiddo & Ramakrishnan Skirant "Discovering Predictive Association Rules" AAAI, 1998 Manish Mehta, Rakesh Agrawal & Jorma Rissanen "SLIQ: A Fast Scalable Classifier for Data Mining" 5th International Conference on Extending Database Technology, Avignon, France, 1996 Manish Mehta, Jorma Rissanen & Rakesh Agrawal "MDL-based Decision Tree Prunning" International Conference on KDD and Data Mining (KDD'95), Montreal, Canada, 1995 Andreas Mueller "Fast Sequential and Parallel Algorithms for Association Rule Mining: A Comparison" Department of Computer Science, University of Maryland - College Park, CS-TR-3515 College Park, MD, August 1995
5 Raymond T. Ng, Laks V.S. Lakshmanan, Jiawei Han & Alex Pang "Exploratory Mining and Pruning Optimizations of Constrained Association Rules" Proceedings of the ACM SIGMOD Conference on Management of Data, SIGMOD 98 ACM, 1998 "Developing Applications with Oracle Lite" "Oracle7 TM Server SQL Reference" Oracle Corporation, 1996 "Oracle JDBC Drivers" "Oracle Lite API Reference Guide" "Oracle Lite JAVA Developer s Guide" "Oracle Lite SQL Language Help" "SQL*Plus User s Guide and Reference" Oracle Corporation, 1996 Carlos Ordonez & Edward Omiecinski "Image Mining: A New Approach for Data Mining" Georgia Institute of Technology, 1998 Kevin T. Owens "Building Intelligent Databases with ORACLE PL/SQL, Triggers & Stored Procedures" USA: Prentice Hall PTR, 1996 [ISBN ]. Jong Soo Park, Ming-Syan Chen & Philip S. Yu "An Effective Hash-Based Algorithm for Mining Association Rules" Proceedings of the ACM SIGMOD Conference on Management of Data San Jose, California, USA, May 1995 Jong Soo Park, Ming-Syan Chen & Philip S. Yu "Using a Hash-Based Method with Transaction Trimming for Mining Association Rules" IEEE Transactions on Knowledge and Data Engineering September/October 1997 Jong Soo Park, Philip S. Yu & Ming-Syan Chen "Mining Asociation Rules with Adjustable Accuracy" Proceedings of the 6th International Conference on Information and Knowledge Management Las Vegas, Nevada, USA, 1997 James R. Parker "Algorithms for Image Processing and Computer Vision" USA: John Wiley & Sons, 1997
6 Roger S. Pressman "Ingeniería del Software. Un enfoque práctico" McGraw-Hill, 1993 [3ª edición] J. Ross Quinlan "Induction of Decision Trees" Machine Learning 1:81-106, Kluwer Academic Publishers, 1986 J. Ross Quinlan "C4.5: Programs for Machine Learning" San Francisco (California): Morgan Kaufmann Publishers, 1993 Roy Rada "Corporate Shortcut to Standarization" Communications of the ACM, January 1998 James S. Ribeiro, Kenneth A. Kaufman & Larry Kerschberg "Knowledge Discovery from Multiple Databases" First International Conference on Knowledge Discovery (KDD-95), August 1995 Elaine Rich & Kevin Knight "Inteligencia Artificial" McGraw-Hill Interamericana de España, 1994 [2ª edición] Sunita Sarawagi, Shiby Thomas & Rakesh Agrawal "Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications" IBM Research Report. IBM Almaden Research Center, San Jose, California, 1998 S.Z. Selim & K. Alsultan "A Simulated Annealing for the Clustering Problem" Pattern Recognition 24, 1991, pgs Sabrina Sestito & Tharam S. Dillon "Automated Knowledge Acquisition" Australia: Prentice Hall Series in Computer Science and Engineering, 1994 John Shafer, Rakesh Agrawal & Manish Mehta "SPRINT: A Scallable Parallel Clasifier for Data Mining" Proceedings of the 22nd VLDB Conference, India, 1996 Sandeep Singhal & Bihn Nguyen, eds. "The Java Factor" Communications of the ACM, June 1998 Ramakrishnan Skirant & Rakesh Agrawal "Mining generalized association rules" 21st Intl. Conference on Very Large Databases, Zürich, Ramakrishnan Skirant & Rakesh Agrawal "Mining Quantitative Association Rules in Large Relational Tables" Proceedings of the ACM SIGMOD Conference on Management of Data, SIGMOD'96 ACM, 1996
7 Ramakrishnan Skirant, Quoc Vu & Rakesh Agrawal "Mining Association Rules with Item Constraints" KDD 97 [AAAI Workshop on Knowledge Discovery in Databases] AAAI, 1997 Bill Whiting, Bryan Morgan & Jeff Perkins: "Teach yourself ODBC Programming in 21 days" USA: Sams Publishing, 1996 Larry Wos "Programs that offer fast, flawless, logical reasoning" Communications of the ACM, June 1998 Peggy Wright "Knowledge Discovery in Databases" CROSSROADS The ACM Student Magazine, Winter 1998 Zhaohui Zhang, Yuchang Lu & Bo Zhang: "An effective partitioning-combining algorithm for discovering quantitative association rules" En "KDD. Techniques and Applications", H. Lu et al. eds., World Scientific, 1997.
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