Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Crowd-Powered Data Mining   Many data mining tasks cannot be completely addressed by auto- mated
processes, such as sentiment analysis and image classification. Crowdsourcing
is an effective way to harness the human cognitive ability to process these
machine-hard tasks. Thanks to public crowdsourcing platforms, e.g., Amazon
Mechanical Turk and Crowd- Flower, we can easily involve hundreds of thousands
of ordinary workers (i.e., the crowd) to address these machine-hard tasks. In
this tutorial, we will survey and synthesize a wide spectrum of existing
studies on crowd-powered data mining. We first give an overview of
crowdsourcing, and then summarize the fundamental techniques, including quality
control, cost control, and latency control, which must be considered in
crowdsourced data mining. Next we review crowd-powered data mining operations,
including classification, clustering, pattern mining, machine learning using
the crowd (including deep learning, transfer learning and semi-supervised
learning) and knowledge discovery. Finally, we provide the emerging challenges
in crowdsourced data mining.

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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: physics