Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Merging $K$-means with hierarchical clustering for identifying
  general-shaped groups   Clustering partitions a dataset such that observations placed together in a
group are similar but different from those in other groups. Hierarchical and
$K$-means clustering are two approaches but have different strengths and
weaknesses. For instance, hierarchical clustering identifies groups in a
tree-like structure but suffers from computational complexity in large datasets
while $K$-means clustering is efficient but designed to identify homogeneous
spherically-shaped clusters. We present a hybrid non-parametric clustering
approach that amalgamates the two methods to identify general-shaped clusters
and that can be applied to larger datasets. Specifically, we first partition
the dataset into spherical groups using $K$-means. We next merge these groups
using hierarchical methods with a data-driven distance measure as a stopping
criterion. Our proposal has the potential to reveal groups with general shapes
and structure in a dataset. We demonstrate good performance on several
simulated and real datasets.

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The categories of this paper are: funct-an
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: cmp-lg