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  4. One click mining-interactive local pattern discovery through implicit preference and performance learning
 
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2013
Conference Paper
Title

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

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 workows 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 interesti ngness 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.
Author(s)
Boley, Mario  
Mampaey, M.
Kang, B.
Tokmakov, P.
Wrobel, Stefan  
Mainwork
IDEA '13, Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics  
Conference
International Conference on Knowledge Discovery and Data Mining (KDD) 2013  
DOI
10.1145/2501511.2501517
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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