------------------------------------------------------------------------
Pacific-Asia Conference on Knowledge Discovery and Data Mining
Visit the PAKDD-01 Home Page:
http://www.csis.hku.hk/pakdd01/
Early Bird Registration Due March 16:
http://www.csis.hku.hk/pakdd01/page-registration.htm
There are several international fairs, exhibitions, and conferences
(some related to PAKDD, including DASFAA and IFIP DS-9) in Hong Kong
during the month of April 2001. Reserve your accommodation soon:
http://www.csis.hku.hk/pakdd01/page-hotel.htm
------------------------------------------------------------------------
CONFERENCE BACKGROUND, THEMES AND TARGET AUDIENCE:
PAKDD 2001, Hong Kong, 16--18 April, is the Fifth Pacific-Asia
Conference on Knowledge Discovery and Data Mining. It is the successor
of earlier PAKDD conferences held in Singapore (1997), Melbourne,
Australia (1998), Beijing, China (1999), and Kyoto, Japan (2000).
PAKDD 2001 will be an international forum for the sharing of original
research results and practical development experiences. Practitioners
and researchers alike will benefit from the technical program and
scholarly exchange.
KEYNOTE PRESENTATIONS
Incompleteness in Data Mining
Professor H. V. Jagadish, a world leading researcher in data mining
from the University of Michigan thinks that the current data mining
techniques, with carefully engineered algorithms, are extremely
expensive. Since the central goal of data mining is to find SOME
interesting patterns, he will argue that it is not necessary to find
ALL of them -- is incompleteness the right answer ?
Mining E-commerce Data: The Good, the Bad, and the Ugly
Dr. Ronny Kohavi, Director of Data Mining at Blue Martini Software,
is an industrial leader in Data Mining software. He will talk about
the lessons, stories, and challenges of data mining based on mining
real data. According to Ronny e-commerce provides all the right
ingredients for data mining (the Good). So, what are the Bad and the
Ugly ?
Seamless Integration of Data Mining with DBMS and Applications
Professor Hongjun Lu of The Hong Kong University of Science and
Technology, an internationally renowned researcher in data mining,
will argue that most data mining algorithms can only be loosely
coupled with data infrastructures in organizations and are difficult
to infuse into existing mission-critical applications. He will
propose to tackle the problem of integration of data mining
with DBMS and applications from three directions.
TECHNICAL PRESENTATIONS:
The technical program features 38 regular presentations and 22 short
presentations. Topics include: Web and Text Mining; Sequence, Spatial
and Temporal Mining; Applications and Tools; and more.
For a complete list of papers visit:
http://www.csis.hku.hk/pakdd01/page-program.htm
INDUSTRIAL TRACK PRESENTATIONS:
PAKDD01 has created a new track for practitioners, vendors and users
to present experiences in data mining in their respective areas.
- Data Mining at Standard Chartered Bank, Steven Parker, Standard and
Charter
- Improving web design - mining web data at SCMP.com, H.P.Lo, City U
of Hong Kong
- Data Mining Application and Implementation in Banking, a Case
Study, Dick Cheng, SAS Institute, Australia
- Data Mining Application in Internet Polling, Dennis Pang,
Superpoll, Taiwan
- and many more ....
TUTORIALS:
An Introduction to MARS
- Dr. Dan Steinberg, CEO of Salford Systems, USA
Static and Dynamic Data Mining Using Advanced Machine Learning
Methods
- Professor Ryszard S. Michalski, George Mason University, USA
Sequential Pattern Mining: From Shopping History Analysis to
Weblog Mining and DNA Mining
- Professor Jiawei Han and Mr. Jian Pei, Simon Fraser University,
Canada
Recent Advances in Data Mining Algorithms for Large Databases
- Dr. Rajeev Rastogi and Dr. Kyuseok Shim, AT&T Bell Lab
& KAIST, Korea.
Web Mining for E-Commerce
- Professor Jaideep Srivastava, University of Minnesota, USA
From Evolving Single Neural Networks to Evolving Ensembles
- Professor Xin Yao, The University of Birmingham, United Kingdom.
WORKSHOPS:
Spatial and Temporal Data;
Statistical Techniques in Data Mining;
Data Mining an Electronic Business.
ORGANIZATION:
Conference Chairs:
Chung-Jen Tan (University of Hong Kong and IBM Watson)
Jiawei Han (Simon Fraser University, Canada)
Program Committee Chairs:
David Cheung (University of Hong Kong)
Qing Li (City University of Hong Kong)
Graham Williams (CSIRO, Australia)
Tutorial Chair:
Joshua Z Huang (University of Hong Kong)
Workshop Chair:
Michael K Ng (University of Hong Kong)
Industrial Chair:
Joseph Fong (City University of Hong Kong)
Demonstration Chair:
Jiming Liu (Baptist University of Hong Kong)
Local arrangements Chairs:
Ronnie Cheung (Hong Kong Poly University)
Ben Kao (University of Hong Kong)
Publicity Chairs:
Vincent Ng (Hong Kong Poly University)
Rohan Baxter (CSIRO, Australia)
Hiroyuki Kawano (Kyoto University, Japan)
Treasurer:
Ada Fu (Chinese University of Hong Kong)
------------------------------------------------------------------------
