KDD Nuggets 95:11, e-mailed 95-05-23 Contents: * T. Sellis, Warning: Plagiarism case * K. Ong, CFP: Workshop on the Integration of Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD) * G. Williams, CFP: Australian AI'95 * J. Elder, Short Course on Computer-Aided Pattern Discovery * M. Feldens, Question: Fraud detection systems ? * GPS, Neural networks FAQ at http://wwwipd.ira.uka.de/~prechelt/FAQ/neural-net-faq.html The KDD Nuggets is a moderated mailing list for news and information relevant to Knowledge Discovery in Databases (KDD), also known as Data Mining, Knowledge Extraction, etc. Relevant items include tool announcements and reviews, summaries of publications, information requests, interesting ideas, clever opinions, etc. Please include a descriptive subject line in your submission. Nuggets frequency is approximately bi-weekly. Back issues of Nuggets, a catalog of S*i*ftware (data mining tools), references, FAQ, and other KDD-related information are available at Knowledge Discovery Mine, URL http://info.gte.com/~kdd/ or by anonymous ftp to ftp.gte.com, cd /pub/kdd, get README E-mail add/delete requests to kdd-request@gte.com E-mail contributions to kdd@gte.com -- Gregory Piatetsky-Shapiro (moderator) ********************* Official disclaimer *********************************** * All opinions expressed herein are those of the writers (or the moderator) * * and not necessarily of their respective employers (or GTE Laboratories) * ***************************************************************************** ~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Discovery consists of seeing what everybody has seen and thinking what nobody has thought. Albert von Szent-Gyorgyi >~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Date: Sat, 6 May 1995 07:16:23 +0300 From: Timos Sellis To: kdd@gte.com Subject: For KDD nuggets We are investigating in Athens the case of a person submitting plagiarized papers to various conferences and journals around the world. Till this investigation is over, I would like to warn Editors of journals and Program Committe Chairs about papers submitted by ANYONE (the person has been using various names and affiliations) requesting an acknowledgement to the following address: 77 Aristeidou Str. Kallithea, GR-17671 GREECE If you encounter such a submission, please notify me to the above address. This is important to protect other people whose names may be unwillingly used in these articles. Sincerely, Prof. Timos Sellis Tel. +30-1-7728-180 Dept. of Electrical and Computer Engin. FAX. +30-1-7784-578 Division of Computer Science timos@theseas.ntua.gr National Technical University of Athens WWW: http://www.ntua.gr/~timos Zographou 157 73, Athens GREECE >~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Date: Mon, 8 May 95 17:02:53 CDT From: ong@mcc.com (Kayliang Ong) To: kdd@gte.com Subject: *** Deadline Extended *** (Second Call) CFP: First International Workshop on the Integration of Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD) ================================================================================ KDOOD Announcements ================================================================================ *** NEW *** Due to the extended deadline for the DOOD'95 conference, we have postponed the deadline for workshop from June 1, 1995 to <<< July 1, 1995. >>> *** NEW *** Contact information has been changed. *** NEW *** This KDOOD CALL-FOR-PAPER can also be found on WWW: http://www.cs.concordia.ca/kdood.html This www page consists of more colorful description of the CFP. For those who are interested to submit papers or participate in the workshop, the web page will be updated frequently to reflect any new information. ** PLEASE CONTINUE TO MONITOR THE DEVELOPMENT ** ** BY VISITING THE PAGE. ** ================================================================================ CALL FOR PAPERS First International Workshop on the Integration of Knowledge Discovery with Deductive and Object-Oriented Databases (KDOOD) December 8, 1995, Singapore OBJECTIVES Knowledge discovery from databases and Deductive and Object-Oriented Databases (DOOD) are two promising research areas that have so far been growing rather independently of each other. However, lots of evidence suggests that the two areas can be mutually beneficial. For example, DOOD techniques may be used to facilitate knowledge discovery, whereas knowledge discovery techniques may help knowledge-base construction, and thus enhance and/or challenge the deductive and object-oriented database techniques. The objective of this workshop is to explore how knowledge discovery and DOOD can be integrated. FORMAT The workshop will be held immediately following the DOOD'95 conference. The plan is to have either a half-day or a full-day session depending on the number of submissions and the level of participation. If the number of submissions is large, we may categorize the papers in presentation papers, poster papers and system/prototype descriptions/demonstrations. Proposals for panel discussions are welcome. The final program of the workshop will be decided only after all the papers have been reviewed. The program will be formatted in a way that will encourage open discussions. The workshop also plans to invite one or two pioneering researchers in the area as keynote speakers. TOPICS The topics of interest include but are not limited to the following: 1. Knowledge-base construction by KDD. 2. Rule-guided induction. 3. Integration of induction and deduction techniques. 4. Extension of DOOD systems for KDD. 5. Knowledge-merging: merging deduction rules and discovered rules. 6. Induction from data and schema. 7. Applications of discovered knowledge in DOOD (including semantic query optimization, intelligent query answering, data purification, etc.). 8. Uncertainty handling in data mining and DOOD. 9. KDD tools for DOOD. SUBMISSION AND REVIEWS OF THE PAPERS Authors are invited to submit original research contributions. Each paper should not be longer than 10 pages. WE ENCOURAGE ELECTRONIC SUBMISSIONS IN THE FORM OF POSTSCRIPT, LATEX ETC. but limited to the std 8/5x11 size paper. If hardcopies are submitted, four copies will be required. Each submitted paper will be reviewed by at least three program committee members. PUBLICATIONS To be decided based on the number of submissions. An arrangement will likely be made with the DOOD conference organizers. ATTENDANCE At least one author of the papers selected for paper and/or poster presentation must attend the workshop. Researchers who are willing to contribute in the discussions are encouraged to participate. Workshop participants are not required to register and attend DOOD'95 conference. However, a discount will be given if they attend both conference and workshop. PROGRAM COMMITTEE Rakesh Agrawal, IBM, USA Paolo Atzeni, Universita' La Sapienza, Italy Ron Brachman, AT&T Bell Laboratories JiaWei Han, Simon Fraser University, Canada Willi Kloesgen, GMD, Germany Laks V.S. Lakshmanan, Concordia University, Canada Raymond Ng, U. British Columbia, Canada Shojiro Nishio, Osaka University, Japan KayLiang Ong, Trilogy, USA Beng-Chin Ooi, NUS, Singapore Wei-Min Shen, ISI/USC, USA Evangelos Simoudis, Lockheed, USA Shalom Tsur, Argonne National Research Lab, USA Carlo Zaniolo, UCLA, USA IMPORTANT DATES 1 July 1995 - Final submission date for papers 21 August 1995 - Notification 4 September 1995 - Workshop programs completed 18 September 1995 - Camera ready 8 December 1995 - Workshop ORGANIZING COMMITTEE Jiawei Han, Simon Fraser University, Canada. Laks V.S. Lakshmanan, Concordia University, Canada. Raymond Ng, University of British Columbia, Canada. KayLiang Ong, Trilogy Development Group, USA. For further information and ELECTRONIC submission, please send to: KayLiang Ong Trilogy Development Group 6034, West Courtyard Drive, Suite 130 Austin, Texas 78730 Phone: 512-794-5900 (After May 22, 1995) 512-794-9491 (Before May 22, 1995) Email: ong@trilogy.com For HARDCOPY submission, please send to: Raymond Ng Department of Computer Science University of British Columbia Vancouver, B.C., Canada V6T 1Z4 Phone: 604-822-2394. Fax: 604-822-5485. Email: rng@cs.ubc.ca >~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Date: Tue, 9 May 1995 12:26:28 +1000 From: Graham.Williams@cbr.dit.csiro.au To: kdd@gte.com Subject: CONFERENCE: Australian AI'95 Content-Type: text Content-Length: 1803 The following may be of interest to readers of KDD. =========================================================================== AI'95 is the Eighth Australian Joint Conference on Artificial Intelligence. This annual conference is the largest Australian AI conference and attracts many overseas participants. It covers a broad range of AI topics, including Machine Learning, Knowledge Acquistion, and Data Mining. This year, the conference is being hosted by the Department of Computer Science, Australian Defence Force Academy (University College, University of New South Wales), Canberra, 13 -- 17 November 1995. A Web site has been set up to provide up-to-date information about the conference. It will be regularly maintained to include Tutorial, Workshop, Keynote Speaker, Programme, and Tourist information. It can be visited at: The main theme of AI'95 is ``bridging the gaps,'' i.e., bridging the gap between the classical symbolic approach and other subsymbolic approaches, such as artificial neural networks, evolutionary computation and artificial life, to AI, and bridging the gap between the AI theory and real world applications. The goals of the conference are to promote cross-fertilisation among different approaches to AI and provide a common forum for both researchers and practitioners in the AI field to exchange new ideas and share their experience. =========================================================================== -- Graham.Williams@cbr.dit.csiro.au ,--_|\ Tel: +61 6 216 7042 CSIRO Division of Information Technology / \ Fax: +61 6 216 7111 CS&IT Building, North Road, Aust Nat Univ \_.--_*/ GPO Box 664 Canberra v ACT 2601 Australia >~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ From: elder@masc4.rice.edu (John Elder) Subject: Short Course announcement To: kdd@gte.com Date: Wed, 17 May 1995 16:08:47 -0500 (CDT) Making Sense of Data: Computer-Aided Pattern Discovery A Rice University Short Course, July 20-21, 1995 Is useful information hidden in your collection of data? How can you identify patterns and trends to classify a new case and give it contextual meaning? This two-day intensive short course will survey new methods of computer-aided data analysis that can enable researchers and analysts in many different professions to classify data and make useful estimates and forecasts. These techniques, drawn from the fields of statistics, machine learning, data mining, and inductive modeling, have been applied successfully to many real-world problems, ranging from medical diagnosis and adaptive flight control, to machine fault identification and selection of stocks and bonds. Course Content The course will focus on the leading methods used in industry and academia. Instructors will describe the key inner workings of various algorithms, compare their merits, and demonstrate their effectiveness on practical applications. They will first review classical statistical techniques, both linear and nonparametric, then outline the ways in which these basic tools are modified and combined into more modern methods. The instructors will pay particular attention to four powerful approaches: kernels, neural networks, polynomial networks, and decision trees, and will use sample scientific, medical, and financial applications to demonstrate general techniques (such as scientific visualization) and "tricks of the trade" employed by experienced analysts. Data Workshop At a "Data Workshop" and demonstration on the first evening of the course, participants may try some of these methods using their own data. (Please indicate on the registration form if you plan to bring data for the workshop.) Who Should Attend Those from industry and academia who work with data and wish to understand recent developments in pattern discovery, data mining, and inductive modeling. At the conclusion of this course, they should be able to discern the strengths of competing methods and select the appropriate tools for their applications. Participants should have prior working experience with computers and knowledge of, or interest in, applied statistical techniques. Course Outline Pattern Discovery: An Overview Inducing Models from Data: Benefits and Dangers Related Fields: Statistics, Machine Learning, Data Mining, and Artificial Intelligence Data Issues Case Diagnostics Feature Creation and Selection Classical Statistical Techniques Linear: Regression and Discriminant Analysis Nonparametric: Scatterplot Smoothers, Nearest Neighbors, Kernels Other Key Tools: Optimization, Clustering Modern Methods ASH* (Average Shifted Histograms) Neural Networks* Polynomial Networks* (ASPN, AIM) Decision Trees* (CART) Brief Survey of Other Methods Projection Pursuit MARS (Multivariate Adaptive Regression Splines) UPM* (Universal Process Modeling) Radial Basis Functions Examples of Applications Diagnosing Breast Cancer Estimating Air Quality Classifying Bat Species Investing in the Bond Market Instructors The instructors and guest lecturer each have more than a decade of experience in applying adaptive, data-driven techniques to practical problems, and have developed some of the leading methods covered in this course. Dr. John F. Elder is Research Scientist in the Department of Computational and Applied Mathematics and the Center for Research on Parallel Computation at Rice University. He is the author of three book chapters and numerous articles on adaptive methods of pattern discovery, and is technical chair of the Adaptive and Learning Systems Group of the IEEE Systems, Man, and Cybernetics Society. He has been a research scientist for an engineering consulting business and director of research for an investment management firm, and has a Ph.D. in Systems Engineering from the University of Virginia. Paul Hess has been President of Hess Consulting in Herndon, Va. since 1991. He was a research scientist for an engineering consulting firm and co-founder of AbTech Corporation, a leading maker of artificial intelligence software. Guest lecturer Dr. David W. Scott is Professor of Statistics and former Chair of the Statistics Department at Rice University. He is the author of the 1992 book Multivariate Density Estimation, as well as numerous articles. Dr. Scott is editor of the journal Computational Statistics and is on the editorial board of John Wiley & Sons. Additional Information When and Where: Thursday-Friday, July 20-21, 1995, 9:00 a.m.-12:00 noon and 1:15-4:00 p.m., on the Rice University campus, 6100 Main Street, Houston, Texas. The Data Workshop will be held Thursday evening, 6:00-8:00 p.m. Fee: $395. Early discount fee: $345 for those registering by June 15. Fee for graduate students: $245. Fee includes lecture notes, lunches at the Rice Faculty Club, and the Data Workshop. CEUs: 1.4 Refund Policy: Course fee will be refunded in full if enrollment is canceled in writing by June 30. If you drop the course June 30 through July 10, a refund will be issued only if a replacement for you can be found, and a $95 processing fee will be deducted from your refund. Refunds will not be issued after July 10. Accommodations: A block of rooms at a special rate of $65 has been reserved at the Houston Plaza Hilton, located in the Texas Medical Center, within walking distance of Rice University. Reservations must be made by July 5, 1995. Contact the hotel directly at (713) 524-6633. A list of other nearby hotels will be mailed upon request. For more information For information on registration, contact Rice University School of Continuing Studies, (713) 520-6022 or 527-4803 E-mail: scs@rice.edu For more information on course content, contact Dr. John Elder, (713) 285-5182 E-mail: elder@rice.edu >~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Date: Tue, 16 May 95 15:33:53 EST From: feldens@inf.ufrgs.br (Miguel Artur Feldens) To: kdd@gte.com Subject: Fraud detection systems Content-Type: text Content-Length: 395 Mr. Piatetsky-Shapiro I would be very gratefull to receive any information about KDD related with Fraud Detection. My current interest is about such kind of discovery in low quality (maybe highly frauded) data sets. Could you or anyone reading KDD-Nuggets list point some articles? Thanks in advance! Miguel Artur Feldens feldens@inf.ufrgs.br Universidade Federal do Rio Grande do Sul Brazil >~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Date: Tue, 2 May 1995 13:02:28 -0400 From: gps0 (Gregory Piatetsky-Shapiro) To: kdd Subject: Neural networks FAQ and Software URLs Content-Type: text Content-Length: 597 The FAQ posting is archived in the periodic posting archive on host rtfm.mit.edu (and on some other hosts as well). Look in the anonymous ftp directory "/pub/usenet/news.answers", the filename is 'neural-net-faq'. If you do not have anonymous ftp access, you can access the archive by mail server as well. Send an E-mail message to mail-server@rtfm.mit.edu with "help" and "index" in the body on separate lines for more information. The monthly posting is also available as a hypertext document in WWW (World Wide Web) under the URL http://wwwipd.ira.uka.de/~prechelt/FAQ/neural-net-faq.html For NN software: free http://wwwipd.ira.uka.de/~prechelt/FAQ/nn5.html#A18 commercial http://wwwipd.ira.uka.de/~prechelt/FAQ/nn6.html#A19