Title
Mining itemsets in the presence of missing values Mining itemsets in the presence of missing values
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
conferenceObject
Publication
New York, N.Y. :ACM, [*]
Subject
Computer. Automation
Source (book)
Proceedings of the ACM Symposium on Applied Computing
ISBN
978-1-59593-480-2
ISI
000268215700081
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
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.
E-info
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Handle