Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Clustering of longitudinal data: A tutorial on a variety of approaches   During the past two decades, methods for identifying groups with different
trends in longitudinal data have become of increasing interest across many
areas of research. To support researchers, we summarize the guidance from the
literature regarding longitudinal clustering. Moreover, we present a selection
of methods for longitudinal clustering, including group-based trajectory
modeling (GBTM), growth mixture modeling (GMM), and longitudinal k-means (KML).
The methods are introduced at a basic level, and strengths, limitations, and
model extensions are listed. Following the recent developments in data
collection, attention is given to the applicability of these methods to
intensive longitudinal data (ILD). We demonstrate the application of the
methods on a synthetic dataset using packages available in R.

20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio