KDD Nuggets Vol. 1, No. 9 -- 17 December, 1993 Contents: * Usama Fayyad -- KDD-94 Call For Papers -- due March 1, 1994 * M. Hadjimichael -- Electronic Bulletin of the Rough Set Community * R. Golan -- Canadian Stock Market Data The KDD Nuggets is an informal list for the dissemination of information relevant to Knowledge Discovery in Databases (KDD), such as announcements of conferences/workshops, tool reviews, application examples, information requests, interesting ideas, outrageous opinions, etc. E-mail contributions and requests to be added/deleted to kdd%eureka@gte.com Season's Greetings and a Happy 1994 to all! -- Gregory Piatetsky-Shapiro KDD Nuggets archive will soon be available by FTP (and maybe gopher) -------------------------------------------------- ============================================================================ C a l l F o r P a p e r s ============================================================================ KDD-94: AAAI Workshop on Knowledge Discovery in Databases Seattle, Washington, July 31-August 1, 1994 =========================================== Knowledge Discovery in Databases (KDD) is an area of common interest for researchers in machine learning, machine discovery, statistics, intelligent databases, knowledge acquisition, data visualization and expert systems. The rapid growth of data and information created a need and an opportunity for extracting knowledge from databases, and both researchers and application developers have been responding to that need. KDD applications have been developed for astronomy, biology, finance, insurance, marketing, medicine, and many other fields. Core Problems in KDD include representation issues, search complexity, the use of prior knowledge, and statistical inference. This workshop will continue in the tradition of the 1989, 1991, and 1993 KDD workshops by bringing together researchers and application developers from different areas, and focusing on unifying themes such as the use of domain knowledge, managing uncertainty, interactive (human-oriented) presentation, and applications. The topics of interest include: Applications of KDD Techniques Interactive Data Exploration and Discovery Foundational Issues and Core Problems in KDD Machine Learning/Discovery in Large Databases Data and Knowledge Visualization Data and Dimensionality Reduction in Large Databases Use of Domain Knowledge and Re-use of Discovered Knowledge Functional Dependency and Dependency Networks Discovery of Statistical and Probabilistic models Integrated Discovery Systems and Theories Managing Uncertainty in Data and Knowledge Machine Discovery and Security and Privacy Issues We also invite working demonstrations of discovery systems. The workshop program will include invited talks, a demo and poster session, and panel discussions. To encourage active discussion, workshop participation will be limited. The workshop proceedings will be published by AAAI. As in previous KDD Workshops, a selected set of papers from this workshop will be considered for publication in journal special issues and as chapters in a book. Please submit 5 *hardcopies* of a short paper (a maximum of 12 single-spaced pages, 1 inch margins, and 12pt font, cover page must show author(s) full address and E-MAIL and include 200 word abstract + 5 keywords) to reach the workshop chairman on or before March 1, 1994. Usama M. Fayyad (KDD-94) | Fayyad@aig.jpl.nasa.gov AI Group M/S 525-3660 | Jet Propulsion Lab | (818) 306-6197 office California Institute of Technology | (818) 306-6912 FAX 4800 Oak Grove Drive | Pasadena, CA 91109 | ************************************* I m p o r t a n t D a t e s ********** * Submissions Due: March 1, 1994 * * Acceptance Notice: April 8, 1994 Final Version due: April 29, 1994 * ****************************************************************************** Program Committee ================= Workshop Co-Chairs: Usama M. Fayyad (Jet Propulsion Lab, California Institute of Technology) Ramasamy Uthurusamy (General Motors Research Laboratories) Program Committee: Rakesh Agrawal (IBM Almaden Research Center) Ron Brachman (AT&T Bell Laboratories) Leo Breiman (University of California, Berkeley) Nick Cercone (University of Regina, Canada) Peter Cheeseman (NASA AMES Research Center) Greg Cooper (University of Pittsburgh) Brian Gaines (University of Calgary, Canada) Larry Kerschberg (George Mason University) Willi Kloesgen (GMD, Germany) Chris Matheus (GTE Laboratories) Ryszard Michalski (George Mason University) Gregory Piatetsky-Shapiro (GTE Laboratories) Daryl Pregibon (AT&T Bell Laboratories) Evangelos Simoudis (Lockheed Research Center) Padhraic Smyth (Jet Propulsion Laboratory) Jan Zytkow (Wichita State University) ============================================================================ -------------------------------------------------- ===================================================================== /------------------------------------------------\ < Electronic Bulletin of the Rough Set Community > \------------------------------------------------/ [ Editor: M. Hadjimichael ] [ Asst Editor: R. Golan ] [ University of Regina, Sask ] email: roughset@cs.uregina.ca ftp: ftp.cs.uregina.ca:/pub/ebrsc gopher: gopher.cs.uregina.ca ===================================================================== 1. Goals of the Bulletin In its first phase, we see the Bulletin primarily as a means of rapidly communicating abstracts of papers in the Rough Sets field of study (see file rs.intro). These should be abstracts of (1) papers not yet in print, (2) recently published papers, or (3) papers published in difficult-to-obtain journals, and thus inaccessible to the greater scientific community. Additionally, the Bulletin may serve as a means for researchers to present abstracts of work in progress, as a way of initiating further communication in private with other interested individuals. Finally, the Bulletin may be used to communicate announcements of interest to our community, such as conference announcements, calls for papers, etc. The Bulletin will also host an archive, containing back issues of the Bulletin, as well as data and software contributed by the Community. In the future, the Bulletin may be expanded as a discussion list and/or mail server, and perhaps, one day, into a hardcopy version... --------------------------------------------------------------------- 2. Content and Format Submissions to the Bulletin will be made electronically by sending email to the Internet address: roughset@cs.uregina.ca Comments and questions may also be sent to this address. Relevant submissions should concern research involving: 1) the fundamentals of Rough Sets; 2) applications of Rough Sets to other theoretical studies (e.g. Machine Learning, Uncertainty Management, Knowledge Discovery, Image Analysis, etc.); 3) applications of Rough Sets in systems; 4) related research in Uncertainty management. Other relevant submissions would include: 1) conference announcements and Call For Papers; 2) announcements about availability on the archive of data/systems/results. ** If an announcement is particularly urgent, please indicate so in the message, and I will endeavor to distribute it as quickly as possible. The Bulletin will be mailed out approximately once a month - depending on the quantity of submissions. It will be a compilation of abstracts and letters as submitted by the contributor. No editing or reviewing will be done beyond checking appropriateness of content (leniently), simple text formatting, and spell-checking. The Bulletin will be sent out as a simple ASCII text file - no processing or compilation will be required to read it. In the future, perhaps we may consider also generating a Postscript version in a prettier format. --------------------------------------------------------------------- 3. Editors At RSKD'93, Robert Golan and I (Michael Hadjimichael) offered our services as editors of the Bulletin. We may be contacted at the Bulletin email address (roughset@cs.uregina.ca), or at our respective personal addresses (mike@cs.uregina.ca and golan@cs.uregina.ca). We are both currently located at: Department of Computer Science University of Regina Regina, SK, S4S 0A2, Canada --------------------------------------------------------------------- 4. Archives Back issues of the Bulletin, along with data and software contributions, will be archived at the University of Regina. They may be retrieved via FTP from: ftp.cs.uregina.ca (142.3.200.53) in the directory pub/ebrsc, and via gopher from: gopher.cs.uregina.ca ------------------------------------------------------------------------ 6. Subscriptions If you have received this issue of the Bulletin via email, then you are already subscribed. If you would like to be removed from our mailing list, simply reply to this message, requesting that your name be removed. If you are not currently subscribed, you may subscribe by sending a brief message to roughset@cs.uregina.ca, stating your full name and email address. ===================================================================== /------------------------------------------------\ < Electronic Bulletin of the Rough Set Community > \------------------------------------------------/ email: roughset@cs.uregina.ca ftp: ftp.cs.uregina.ca:/pub/ebrsc gopher: gopher.cs.uregina.ca ===================================================================== From: Robert Golan Canadian Stock Market Data The data archive of the Electronic Bulletin of the Rough Set Community now contains publically available data from the Canadian Stock Market. This is the data Dr. Ziarko and I (Robert Golan) (at the University of Regina, Sask.) used for our research on stock market analysis. The file "stock.dat" contains the ascii version of the data while "stock.typ" lists the data types. If you have a copy of Datalogic (a commercial package available from Reduct, Inc, Regina, Sask.) use stock.typ to import the stock.dat directly. Information pertaining to this data exists in the following two papers: 1) Temporal Rules Discovery using Datalogic/R+ with Stock Market Data. R. Golan and D. Edwards. RSKD'93 2) An Application of Datalogic/R Knowledge Discovery Tool to Identify Strong Predictive Rules in Stock Market Data. W. Ziarko, R. Golan, D. Edwards. AAAI-93. Please feel free to distribute this data to whomever is interested, of course with our acknowledgment. I would also be interested in any results obtained in order to further evaluate our research efforts. I also have a copy of the raw or non-discretized version of the data. For further information please feel free to contact me @ golan@cs.uregina.ca To download the data use gopher to gopher.cs.uregina.ca, or use ftp to ftp.cs.uregina.ca: pub/ebrsc, with the following instructions: --------begin instructions--------- % ftp ftp.cs.uregina.ca Connected to mercury.cs.uregina.ca 220 mercury.cs.uregina.ca FTP server ready Name (ftp.cs.uregina.ca:telnet): anonymous 331 Guest login ok, type your name as password. Password: <----- your name won't show here 230 Guest login ok, access restrictions apply. Remote system type is UNIX. Using binary mode to transfer files. ftp> ascii 200 Type set to A. ftp> cd pub/ebrsc 250 CWD command successful. ftp> get stock.dat local: stock.dat remote: stock.dat 200 PORT command successful. 150 Opening ASCII mode data connection for 'stock.dat' (27600 bytes). 226 Transfer complete. 27720 bytes received in 0.31 seconds (87.32 Kbytes/s) ftp> get stock.typ local: stock.typ remote: stock.typ 200 PORT command successful. 150 Opening ASCII mode data connection for 'stock.typ' (2031 bytes). 226 Transfer complete. 2212 bytes received in 0.01 seconds (216.02 Kbytes/s) ftp> quit 221 Goodbye. % -----------------end instructions--------------- Rob Golan, Asst Editor, EBRSC. --------------------------------------------------