CANCELLED: AI Seminar for December 5
Thomas Bartold
for December 5th. Professor Kieras will be rescheduled for (early
in) the winter semester.
There is a seminar at 4:30pm on December 5th on data mining, which
AI seminar-goers might find interesting. I attach the announcement
below.
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Professor Laks V.S. Lakshmanan
Department of Computer Science
Concordia University, Montreal
DATE: Tuesday, December 5, 2000
TIME: 4:30 - 5:30 p.m.
ROOM: Room 1311 EECS
THE UNIVERSITY OF MICHIGAN
Department of Electrical Engineering and Computer Science
Computer Science and Engineering Division
CSE FACULTY CANDIDATE SEMINAR
"Constraints and Structures in Data Mining"
Spurred by the potential of discovering interesting and useful
knowledge, substantial research has been done on data mining. Most
previous work essentially falls into one of two "generations". In the
first generation, the focus was on identifying which patterns are
interesting and significant, and devising fast algorithms for mining
them from large data sets. In the second generation, the importance
of integrating data mining with the other key components of the
knowledge discovery (KDD) process has been recognized. One such
component is the underlying DBMS and strategies and architectures for
integrating association rule discovery with the DBMS have been
studied.
In this talk, I will focus on the other, equally (if not more)
important component of KDD -- the human user. Many mining algorithms
devised in the first generation implicitly assume data mining is a
one-shot exercise, as opposed to the iterative exploratory process it
really is. This is a significant shortcoming since mining algorithms
tend to be computationally intensive. Thus, the concerns in
integrating the user in the loop are (i) how can the user control the
nature of the mining computation undertaken by the system at any
point? (ii) how can the user enforce focus on mining based on his
knowledge of application semantics? (iii) how can the user migrate
from specific mining tasks performed, to issuing ad hoc mining
queries? I will discuss how constraints can play a significant role
in addressing these concerns, specifically in the domains of frequent
sets and clustering. Many a time, finding {\em when} a pattern holds
in a data set can be as interesting as the pattern itself: e.g., when
is an item set frequently purchased? I will briefly discuss
algorithms and structures that facilitate this kind of dual mining.
Finally, the data mining process tends to involve multiple mining
tasks, such as classification, frequent sets, data cube, etc. I will
briefly discuss our recent work on a model and algebra for data
mining that neatly integrates many mining tasks into one framework so
the input of one mining task can be the output of another.
Professor Lakshmanan is being considered for a faculty position in
the Department, in the Computer Science and Engineering Division.
Everyone is welcome to attend.