Robust Models and Methods in Clustering over Uncertain Data

Speaker:	Zhenjie ZHANG
		Department of Computer Science
		National University of Singapore

Title:		"Robust Models and Methods in Clustering over
		 Uncertain Data"

Date:		Monday, 5 May 2008

Time:		4:00pm - 5:00pm

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

Abstract:

Uncertain data is now ubiquitous in many database systems and
applications, such as scientific database, sensor network, moving objects
and data stream, due to inaccurate measurement or infrequent data update.
In this talk, I will present our new studies on unsupervised learning over
uncertain data sets. In our study, every uncertain object is modelled as a
sphere in the corresponding space, in which the exact position is bounded
without any underlying distribution assumption. Based on the definition of
uncertainty, different computation models are proposed for unsupervised
learning tasks, including Zero Uncertain Model, Static Uncertain Model,
Dissolvable  Uncertain Model and Reversed Uncertain Model. Each of the
models can be applied to different environments with different
requirements. I will further present some preliminary solutions to the
models with some of the popular learning algorithms, such as k-means
algorithm, EM algorithm.


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Biography:

Zhenjie ZHANG is currently a PhD candidate in the School of Computing,
National University of Singapore, and working with Dr Anthony K.H. Tung.
He received his B.Sc. from the Department of Computer Science and
Engineering, Fudan University, China. His research interests include
general skyline query, unsupervised learning, and game theoretical
analysis over large data. Zhenjie presently has 10 research papers to his
name including papers in major venues such as SIGMOD, ICML and TKDE. He
was a recipient of the prestigious NUS President Fellowship in 2007 and is
a student member of both the ACM and IEEE. More about Zhenjie's research
can be found at www.comp.nus.edu.sg/~zhangzh2/