Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Enhanced Apriori Algorithm for Discovering Frequent Patterns with
  Optimal Number of Scans   Data mining is wide spreading its applications in several areas. There are
different tasks in mining which provides solutions for wide variety of problems
in order to discover knowledge. Among those tasks association mining plays a
pivotal role for identifying frequent patterns. Among the available association
mining algorithms Apriori algorithm is one of the most prevalent and dominant
algorithm which is used to discover frequent patterns. This algorithm is used
to discover frequent patterns from small to large databases. This paper points
toward the inadequacy of the tangible Apriori algorithm of wasting time for
scanning the whole transactional database for discovering association rules and
proposes an enhancement on Apriori algorithm to overcome this problem. This
enhancement is obtained by dropping the amount of time used in scanning the
transactional database by just limiting the number of transactions while
calculating the frequency of an item or item-pairs. This improved version of
Apriori algorithm optimizes the time used for scanning the whole transactional
database.

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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess