Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast, Linear Time, m-Adic Hierarchical Clustering for Search and
  Retrieval using the Baire Metric, with linkages to Generalized Ultrametrics,
  Hashing, Formal Concept Analysis, and Precision of Data Measurement   We describe many vantage points on the Baire metric and its use in clustering
data, or its use in preprocessing and structuring data in order to support
search and retrieval operations. In some cases, we proceed directly to clusters
and do not directly determine the distances. We show how a hierarchical
clustering can be read directly from one pass through the data. We offer
insights also on practical implications of precision of data measurement. As a
mechanism for treating multidimensional data, including very high dimensional
data, we use random projections.

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The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: cs
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph