Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inapproximability for Local Correlation Clustering and Dissimilarity
  Hierarchical Clustering   We present hardness of approximation results for Correlation Clustering with
local objectives and for Hierarchical Clustering with dissimilarity
information. For the former, we study the local objective of Puleo and
Milenkovic (ICML '16) that prioritizes reducing the disagreements at data
points that are worst off and for the latter we study the maximization version
of Dasgupta's cost function (STOC '16). Our APX hardness results imply that the
two problems are hard to approximate within a constant of 4/3 ~ 1.33 (assuming
P vs NP) and 9159/9189 ~ 0.9967 (assuming the Unique Games Conjecture)
respectively.

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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
28
The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg