FRINK Linked Data Fragments server

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• W1994432310 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." @default.
• W1994432310 created "2016-06-24" @default.
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• W1994432310 date "2013-08-11" @default.
• W1994432310 modified "2023-09-23" @default.
• W1994432310 title "One click mining" @default.
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• W1994432310 doi "https://doi.org/10.1145/2501511.2501517" @default.
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