Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DISCERN: Diversity-based Selection of Centroids for k-Estimation and
  Rapid Non-stochastic Clustering   One of the applications of center-based clustering algorithms such as K-Means
is partitioning data points into K clusters. In some examples, the feature
space relates to the underlying problem we are trying to solve, and sometimes
we can obtain a suitable feature space. Nevertheless, while K-Means is one of
the most efficient offline clustering algorithms, it is not equipped to
estimate the number of clusters, which is useful in some practical cases. Other
practical methods which do are simply too complex, as they require at least one
run of K-Means for each possible K. In order to address this issue, we propose
a K-Means initialization similar to K-Means++, which would be able to estimate
K based on the feature space while finding suitable initial centroids for
K-Means in a deterministic manner. Then we compare the proposed method,
DISCERN, with a few of the most practical K estimation methods, while also
comparing clustering results of K-Means when initialized randomly, using
K-Means++ and using DISCERN. The results show improvement in both the
estimation and final clustering performance.

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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol