Title: Objectively evaluating condensed representations and interestingness measures for frequent itemset mining
Authors: Zimmermann, Albrecht # ×
Issue Date: 2015
Publisher: Kluwer Academic Publishers
Series Title: Journal of Intelligent Information Systems vol:45 issue:3 pages:299 -317
Abstract: Itemset mining approaches, while having been studied for more than 15 years, have been evaluated only on a handful of data sets. In particular, they have never been evaluated on data sets for which the ground truth was known. Thus, it is currently unknown whether itemset mining techniques actually recover underlying patterns. Since the weakness of the algorithmically attractive support/confidence framework became apparent early on, a number of interestingness measures have been proposed. Their utility, however, has
not been evaluated, except for attempts to establish congruence with expert opinions. Using an extension of the Quest generator proposed in the original itemset mining paper, we propose to evaluate these measures objectively
for the first time, showing how many non-relevant patterns slip through the cracks.
ISSN: 0925-9902
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Informatics Section
× corresponding author
# (joint) last author

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