Publication
Title
Mining itemsets in the presence of missing values
Author
Abstract
Missing values make up an important and unavoidable problem in data management and analysis. In the context of association rule and frequent itemset mining, however, this issue never received much attention. Nevertheless, the well known measures of support and confidence axe misleading when missing values occur in the data, and more suitable definitions typically don't have the crucial monotonicity property of support. In this paper, we overcome this problem and provide an efficient algorithm, XMiner, for mining association rules and frequent itemsets in databases with missing values. XMiner is empirically evaluated, showing a clear gain over a straightforward baseline-algorithm.
Language
English
Source (book)
Proceedings of the ACM Symposium on Applied Computing
Publication
New York, N.Y. : ACM, 2007
ISBN
978-1-59593-480-2
Volume/pages
(2007), p. 404-408
ISI
000268215700081
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 08.10.2008
Last edited 19.11.2017
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