Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/47475
Title: Discovery of frequent patterns in transactional data streams
Authors: Ng, Willie
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 2010
Source: Ng, W. (2010). Discovery of frequent patterns in transactional data streams. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: 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.
Description: 178 p.
URI: https://hdl.handle.net/10356/47475
DOI: 10.32657/10356/47475
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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