PhD Qualifying Examination


"A Survey on Mining Frequent Itemsets over Data Streams"

By Mr. James Sheung Chak Cheng


Abstract:

Recently, the increasing prominence of data streams has led to the study
of online mining of frequent itemsets, which is an important technique to
a wide range of applications. Unlike mining on static datasets, mining
data streams poses many new challenges. In addition to the one-scan
nature, the unbounded memory requirement and the high data arrival rate,
the combinatorial explosion of itemsets exacerbates the mining task. In
this paper, we survey a number of state-of-the-art algorithms on mining
frequent itemsets over data streams. We organize the stream-mining
techniques into two categories based on the window model that they adopt.
Then, we analyze the algorithms according to whether they are exact or
approximate and whether they are false-positive or false-negative. We
discuss the different issues arisen from the different window models and
the nature of the algorithms.


Date:     		Friday, 9 September 2005

Time:                   3:00p.m.-5:00p.m.

Venue:                  Room 4480
			lifts 25-26

Committee Members:      Dr. Wilfred Ng (Supervisor)
			Dr. Dimitris Papadias (Chairperson)
			Prof. Frederick Lochovsky
			Dr. Nevin Zhang


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