Publication
Title
Efficient pattern mining of uncertain data with sampling
Author
Abstract
Mining frequent itemsets from transactional datasets is a well known problem with good algorithmic solutions. In the case of uncertain data, however, several new techniques have been proposed. Unfortunately, these proposals often suffer when a lot of items occur with many different probabilities. Here we propose an approach based on sampling by instantiating possible worlds of the uncertain data, on which we subsequently run optimized frequent itemset mining algorithms. As such we gain efficiency at a surprisingly low loss in accuracy. These is confirmed by a statistical and an empirical evaluation on real and synthetic data.
Language
English
Source (journal)
Lecture notes in computer science. - Berlin, 1973, currens
Publication
Berlin : 2010
ISSN
0302-9743 [print]
1611-3349 [online]
Volume/pages
6118(2010), p. 480-487
ISI
000281629200048
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 31.03.2011
Last edited 05.08.2017
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