Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient mixture model for clustering of sparse high dimensional binary
  data   In this paper we propose a mixture model, SparseMix, for clustering of sparse
high dimensional binary data, which connects model-based with centroid-based
clustering. Every group is described by a representative and a probability
distribution modeling dispersion from this representative. In contrast to
classical mixture models based on EM algorithm, SparseMix:
  -is especially designed for the processing of sparse data,
  -can be efficiently realized by an on-line Hartigan optimization algorithm,
  -is able to automatically reduce unnecessary clusters.
  We perform extensive experimental studies on various types of data, which
confirm that SparseMix builds partitions with higher compatibility with
reference grouping than related methods. Moreover, constructed representatives
often better reveal the internal structure of data.

25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg