Data Mining for Business Application

Speaker:	Dr. Raymond Chi-Wing WONG
		Department of Computer Science & Engineering
		Hong Kong University of Science & Technology

Title:		"Data Mining for Business Application"

Date:		3 November, 2008

Time:		4:00pm - 5:00pm

Venue:		Lecture Theatre F
		(Leung Yat Sing Lecture Theatre, near lifts 25/26)
		HKUST

Abstract:

In many business applications, data mining can be regarded as a strategy
to understand the past experience which can be used to predict future
events for decision-making. For example, in customer-centric applications,
understanding customer behavior can help companies to improve their
services and thus enhance their competitive position in the market. Most
research works about data mining focus on finding different "patterns"
which can help the process of decision-making. However, it is generally
true that the patterns in themselves do not serve the end purpose of the
business people. We believe that the patterns can aid in more specific
targets. In this talk, we investigate how association rules, one of
popular "patterns" in the literature of data mining, can be used to help
companies for decision-making in applications of inventory control and
marketing. More specifically, maximal-profit item selection with
cross-selling effect (MPIS) problem is investigated. Given a set of items
in the company, the problem is about selecting a subset of items which can
give the maximal profit with the consideration of cross-selling. We prove
that a simple version of this problem is NP-hard. We propose a new
approach to the problem with the consideration of the loss rule - a kind
of association rules to model the cross-selling effect. We show that the
problem can be transformed to a quadratic programming problem. In case
quadratic programming is not applicable, we also propose a heuristics
approach. Besides, we propose an evolutionary approach - genetic algorithm
- to tackle this problem.


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Biography:

Raymond Chi-Wing Wong is an Assistant Professor in Computer Science and
Engineering (CSE) of The Hong Kong University of Science and Technology
(HKUST). He received the BSc, MPhil and PhD degrees in Computer Science
and Engineering in the Chinese University of Hong Kong (CUHK) in 2002,
2004 and 2008, respectively. In 2004-2005, he worked as a research and
development assistant under an R&D project funded by ITF and a local
industrial company called Lifewood.

>From May 2006 to Aug 2006, he was a visiting student of Prof. Jian Pei and
Prof. Ke Wang, at Simon Fraser University in Canada. From Aug 2007 to Sept
2007, he visited IBM T.J. Watson Research Center as a summer intern under
the supervision of Prof. Philip S. Yu. From Jun 2008 to Jul 2008, he
visited Prof. Tamer Ozsu at University of Waterloo as a visiting scholar.
Some of his collaborators are Prof. Ada Fu, Prof. Ke Wang, Prof. Jian Pei,
Prof. Philip S. Yu, Prof. Tamer Ozsu, Prof. Eamonn Keogh, Prof. Yufei Tao,
Prof. Jiuyong Li and Prof. Oscar Au.

He received 19 awards. Within 5 years, he published 19 conference papers
(e.g., SIGKDD, VLDB and ICDM) and 8 journal/chapter papers (e.g., DAMI,
TKDE and VLDB journal). He reviewed papers from conferences and journals
related to data mining and database, including VLDB conference, SIGMOD,
VLDB Journal, TKDE, TKDD, ICDE, SIGKDD, ICDM, DAMI, DaWaK, PAKDD, EDBT and
IJDWM. He is a program committee member of conferences, including VLDB and
APWeb-WAIM, and a referee of journals, including TKDE, DAMI and KAIS. He
also gave presentations in international conferences such as VLDB07,
SIGKDD07, SIGKDD06, ICDM05, SDM05, PAKDD04 and ICDM03.

His research interests include database, data mining, security, data
mining for business applications, customer-centric data analysis, data
warehouse, decision making, data streams, video compression and computer
music.