Organizing Committee
Steering Committee
Program Committee

Papers and Proposals
Call for Papers
Call for Workshop Proposals
Call for Tutorial Proposals
Important Dates
Paper Submission (Closed)
Paper Review (PC Only)

Keynote Speakers
Conference Program
Accepted Papers

Conference Registration
Student Travel Support

PAKDD Workshops

Other Events
PAKDD School
Data Mining Competition

Conference Venue
About Singapore
Tourist Information


PAKDD School

Tentative Programme

Date: Saturday, April 8, 2006
Venue: Microsoft Singapore Pte. Ltd.
1 Marina Boulevard
22 Floor, One Marina Boulevard
Singapore 018989

8.00 am - 8.30 am Registration
8.30 am - 10.00 am Introduction to Data Mining (Koh Hian Chye)
10.00 am - 10.30 am Tea Break
10.30 am - 12.00 noon Hands on session (Koh Hian Chye)
12.00 noon - 1.00 pm Lunch & Posters
1.00 pm - 2.00 pm Advanced Topic I (David Hand)
2.00 pm - 3.00 pm Advanced Topic II (Wang Ke)
3.00 pm - 3.30 pm Tea Break & Posters
3.30 pm - 4.30 pm Advanced Topic III (Barry Shepherd- tentative)
4.30 pm - 6.00 pm Panel Discussion (All speakers)


Koh, Hian Chye
Dr Koh Hian Chye is an Associate Professor and Dean of the School of Business at SIM University (UniSIM). He has published widely in international and regional journals and conferences on topics related to accounting and auditing, business and management, statistical modelling and data mining. His current interest is in the business applications of data mining. Dr Koh frequently acts as a consultant to private companies, large organisations and government agencies. His recent consultancy projects involve data mining applications. He also serves as a trainer in workshops and executive programmes. Dr Koh has recently written a book entitled "Data Mining Applications for Small and Medium Enterprises", which is available at branches of the National Library in Singapore.

Hand, David
David Hand is Professor of Statistics and Head of the Statistics Section at Imperial College London. He has published over twenty books on statistics and related areas, including Principles of Data Mining. He launched the journal Statistics and Computing, and served a term of office as editor of Journal of the Royal Statistical Society, Series C. He was awarded the Thomas L. Saaty Prize for Applied Advances in the Mathematical and Management Sciences in 2001, the Royal Statistical Society's Guy Medal in Silver in 2002, the IEEE International Conference on Data Mining award for Outstanding Contributions in 2004, and was elected Fellow of the British Academy in 2003. He acts as a consultant to a wide range of organisations, including governments, banks, pharmaceutical companies, manufacturing industry, and health service providers.

Shepherd, Barry
Dr Shepherd has over twenty years of software development and consulting experience within the IT industry. He began his career in the UK developing real-time software for GEC-Marconi and conducting research in applied Artificial Intelligence (AI) at Edinburgh University and the Turing Institute. In 1993, he joined ISS as manager of the Knowledge Engineering Program and helped design and launch the Masters degree in Knowledge Engineering as well as lecture and consult in data mining, knowledge-based systems and optimisation techniques. In 2000, he became Vice President of technical marketing at Cygron, a Singapore-based company developing data mining tools and solutions. He now operates as an independent consultant conducting projects and training in customer analytics, data mining and intelligent systems. He works with numerous partners and clients across S.E. Asia and the UK. His recent projects include customer segmentation, building propensity models for direct marketing campaigns and sales data analysis

Wang, Ke
Ke Wang received Ph.D from Georgia Institute of Technology. He is currently a professor at School of Computing Science, Simon Fraser University. Before joining Simon Fraser, he was an associate professor at National University of Singapore. He has taught in the areas of database and data mining.
Ke Wang's research interests include database technology, data mining and knowledge discovery, machine learning, and emerging applications, with recent interests focusing on the end use of data mining. This includes explicitly modeling the business goal (such as profit mining, bio-mining and web mining) and exploiting user prior knowledge (such as extracting unexpected patterns and actionable knowledge). He is interested in combining the strengths of various fields such as database, statistics, machine learning and optimization to provide actionable solutions to real life problems. Ke Wang has published in database, information retrieval, and data mining conferences, including SIGMOD, SIGIR, PODS, VLDB, ICDE, EDBT, SIGKDD, SDM and ICDM. He is an associate editor of the IEEE TKDE journal and has served program committees for international conferences including DASFAA, ICDE, ICDM, PAKDD, PKDD, SIGKDD and VLDB.