Discovery of frequent patterns in transactional data streams
178 p.
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2011
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sg-ntu-dr.10356-474752023-03-04T00:48:02Z Discovery of frequent patterns in transactional data streams Ng, Willie Manoranjan Dash School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition 178 p. 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. DOCTOR OF PHILOSOPHY (SCE) 2011-12-27T08:25:07Z 2011-12-27T08:25:07Z 2010 2010 Thesis Ng, W. (2010). Discovery of frequent patterns in transactional data streams. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/47475 10.32657/10356/47475 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition Ng, Willie Discovery of frequent patterns in transactional data streams |
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178 p. |
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Manoranjan Dash |
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Manoranjan Dash Ng, Willie |
format |
Theses and Dissertations |
author |
Ng, Willie |
author_sort |
Ng, Willie |
title |
Discovery of frequent patterns in transactional data streams |
title_short |
Discovery of frequent patterns in transactional data streams |
title_full |
Discovery of frequent patterns in transactional data streams |
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Discovery of frequent patterns in transactional data streams |
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Discovery of frequent patterns in transactional data streams |
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discovery of frequent patterns in transactional data streams |
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2011 |
url |
https://hdl.handle.net/10356/47475 |
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1759856300724846592 |