Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Big-Data Clustering: K-Means or K-Indicators?   The K-means algorithm is arguably the most popular data clustering method,
commonly applied to processed datasets in some "feature spaces", as is in
spectral clustering. Highly sensitive to initializations, however, K-means
encounters a scalability bottleneck with respect to the number of clusters K as
this number grows in big data applications. In this work, we promote a closely
related model called K-indicators model and construct an efficient,
semi-convex-relaxation algorithm that requires no randomized initializations.
We present extensive empirical results to show advantages of the new algorithm
when K is large. In particular, using the new algorithm to start the K-means
algorithm, without any replication, can significantly outperform the standard
K-means with a large number of currently state-of-the-art random replications.

12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc