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Authors:
Jerzy W. Grzymala-Busse
Patrick G. Clark
Keywords: Data mining; rough set theory; probabilistic approximations; MLEM2 rule induction algorithm; lost values; attribute-concept values
Abstract:
In mining incomplete data, we have a choice for interpretation of missing attribute values. In this paper, we consider two such interpretations: lost values and attribute-concept values. To measure the number of conditions and rules for each interpretation, we conducted experiments on eight incomplete data sets using three kinds of probabilistic approximations: singleton, subset and concept, with eleven values of probability. Using a 5% significance level, the results show that the number of rules is always smaller for attribute-concept values than for lost values. Additionally, the total number of conditions is smaller for attribute-concept values than for lost values for seven out of eight data sets.
Pages: 121 to 126
Copyright: Copyright (c) IARIA, 2015
Publication date: May 24, 2015
Published in: conference
ISSN: 2308-4332
ISBN: 978-1-61208-408-4
Location: Rome, Italy
Dates: from May 24, 2015 to May 29, 2015