Discovery of frequent patterns in transactional data streams
Date of Issue2010
School of Computer Engineering
We investigate the problem of finding frequent patterns in a continuous stream of transactions. In the literature, two prominent approaches are often used: (a) perform approximate counting (e.g., lossy counting algorithm (LCA) of Manku and Motwani, VLDB 2002) by using a lower support threshold than the one given by the user, or (b) maintain a running sample (e.g., reservoir sampling (Algo-Z) of Vitter, TOMS 1985) and generate frequent patterns from the sample on demand. Although both are known to be practically useful, to the best of our knowledge, there has been no comparison carried out between them.
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Nanyang Technological University