Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Pairwise Clustering with Side Information   Pairwise clustering, in general, partitions a set of items via a known
similarity function. In our treatment, clustering is modeled as a transductive
prediction problem. Thus rather than beginning with a known similarity
function, the function instead is hidden and the learner only receives a random
sample consisting of a subset of the pairwise similarities. An additional set
of pairwise side-information may be given to the learner, which then determines
the inductive bias of our algorithms. We measure performance not based on the
recovery of the hidden similarity function, but instead on how well we classify
each item. We give tight bounds on the number of misclassifications. We provide
two algorithms. The first algorithm SACA is a simple agglomerative clustering
algorithm which runs in near linear time, and which serves as a baseline for
our analyses. Whereas the second algorithm, RGCA, enables the incorporation of
side-information which may lead to improved bounds at the cost of a longer
running time.

7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph