Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Clustering-based Framework for Classifying Data Streams   The non-stationary nature of data streams strongly challenges traditional
machine learning techniques. Although some solutions have been proposed to
extend traditional machine learning techniques for handling data streams, these
approaches either require an initial label set or rely on specialized design
parameters. The overlap among classes and the labeling of data streams
constitute other major challenges for classifying data streams. In this paper,
we proposed a clustering-based data stream classification framework to handle
non-stationary data streams without utilizing an initial label set. A
density-based stream clustering procedure is used to capture novel concepts
with a dynamic threshold and an effective active label querying strategy is
introduced to continuously learn the new concepts from the data streams. The
sub-cluster structure of each cluster is explored to handle the overlap among
classes. Experimental results and quantitative comparison studies reveal that
the proposed method provides statistically better or comparable performance
than the existing methods.

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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: stat
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an