Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How to Use Temporal-Driven Constrained Clustering to Detect Typical
  Evolutions   In this paper, we propose a new time-aware dissimilarity measure that takes
into account the temporal dimension. Observations that are close in the
description space, but distant in time are considered as dissimilar. We also
propose a method to enforce the segmentation contiguity, by introducing, in the
objective function, a penalty term inspired from the Normal Distribution
Function. We combine the two propositions into a novel time-driven constrained
clustering algorithm, called TDCK-Means, which creates a partition of coherent
clusters, both in the multidimensional space and in the temporal space. This
algorithm uses soft semi-supervised constraints, to encourage adjacent
observations belonging to the same entity to be assigned to the same cluster.
We apply our algorithm to a Political Studies dataset in order to detect
typical evolution phases. We adapt the Shannon entropy in order to measure the
entity contiguity, and we show that our proposition consistently improves
temporal cohesion of clusters, without any significant loss in the
multidimensional variance.

10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga