Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Hierarchical Clustering for Classification and Anomaly
  Detection   We address the problem of large scale real-time classification of content
posted on social networks, along with the need to rapidly identify novel spam
types. Obtaining manual labels for user-generated content using editorial
labeling and taxonomy development lags compared to the rate at which new
content type needs to be classified. We propose a class of hierarchical
clustering algorithms that can be used both for efficient and scalable
real-time multiclass classification as well as in detecting new anomalies in
user-generated content. Our methods have low query time, linear space usage,
and come with theoretical guarantees with respect to a specific hierarchical
clustering cost function (Dasgupta, 2016). We compare our solutions against a
range of classification techniques and demonstrate excellent empirical
performance.

22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga