Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Discovering of Top-K Sequential Patterns in Event-Based
  Spatio-Temporal Data   We consider the problem of discovering sequential patterns from event-based
spatio-temporal data. The dataset is described by a set of event types and
their instances. Based on the given dataset, the task is to discover all
significant sequential patterns denoting some attraction relation between event
types occurring in a pattern. Already proposed algorithms discover all
significant sequential patterns based on the significance threshold, which
minimal value is given by an expert. Due to the nature of described data and
complexity of discovered patterns, it may be very difficult to provide
reasonable value of significance threshold. We consider the problem of
effective discovering of K most important patterns in a given dataset (that is
discovering of Top-K patterns).

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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: stat
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org