NDLI: One click mining: interactive local pattern discovery through implicit preference and performance learning

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One click mining: interactive local pattern discovery through implicit preference and performance learning

Content Provider	ACM Digital Library
Author	Mampaey, Michael Boley, Mario Kang, Bo Tokmakov, Pavel Wrobel, Stefan
Abstract	It is known that productive pattern discovery from data has to interactively involve the user as directly as possible. State-of-the-art toolboxes require the specification of sophisticated workflows with an explicit selection of a data mining method, all its required parameters, and a corresponding algorithm. This hinders the desired rapid interaction---especially with users that are experts of the data domain rather than data mining experts. In this paper, we present a fundamentally new approach towards user involvement that relies exclusively on the implicit feedback available from the natural analysis behavior of the user, and at the same time allows the user to work with a multitude of pattern classes and discovery algorithms simultaneously without even knowing the details of each algorithm. To achieve this goal, we are relying on a recently proposed co-active learning model and a special feature representation of patterns to arrive at an adaptively tuned user interestingness model. At the same time, we propose an adaptive time-allocation strategy to distribute computation time among a set of underlying mining algorithms. We describe the technical details of our approach, present the user interface for gathering implicit feedback, and provide preliminary evaluation results.
Starting Page	27
Ending Page	35
Page Count	9
File Format	PDF
ISBN	9781450323291
DOI	10.1145/2501511.2501517
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-08-11
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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