Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: K-ANMI: A Mutual Information Based Clustering Algorithm for Categorical
  Data   Clustering categorical data is an integral part of data mining and has
attracted much attention recently. In this paper, we present k-ANMI, a new
efficient algorithm for clustering categorical data. The k-ANMI algorithm works
in a way that is similar to the popular k-means algorithm, and the goodness of
clustering in each step is evaluated using a mutual information based criterion
(namely, Average Normalized Mutual Information-ANMI) borrowed from cluster
ensemble. Experimental results on real datasets show that k-ANMI algorithm is
competitive with those state-of-art categorical data clustering algorithms with
respect to clustering accuracy.

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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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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: econ
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int