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2017 | OriginalPaper | Chapter

Interactive Pattern Sampling for Characterizing Unlabeled Data

Authors : Arnaud Giacometti, Arnaud Soulet

Published in: Advances in Intelligent Data Analysis XVI

Publisher: Springer International Publishing

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Abstract

Many data exploration tasks require a target class. Unfortunately, the data is not always labeled with respect to this desired class. Rather than using unsupervised methods or a labeling pre-processing, this paper proposes an interactive system that discovers this target class and characterizes it at the same time. More precisely, we introduce a new interactive pattern mining method that learns which part of the dataset is really interesting for the user. By integrating user feedback about patterns, our method aims at sampling patterns with a probability proportional to their frequency in the interesting transactions. We demonstrate that it accurately identifies the target class if user feedback is consistent. Experiments also show this method has a good true and false positive rate enabling to present relevant patterns to the user.

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Footnotes
1
It is also possible to set weights to 0 or 1 if the labels of some transactions are already known.
 
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Metadata
Title
Interactive Pattern Sampling for Characterizing Unlabeled Data
Authors
Arnaud Giacometti
Arnaud Soulet
Copyright Year
2017
Book
Advances in Intelligent Data Analysis XVI

Print ISBN: 978-3-319-68764-3

Electronic ISBN: 978-3-319-68765-0

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68765-0_9

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