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Compact Transaction Database for Efficient Frequent Pattern Mining
 

Summary: Compact Transaction Database for Efficient
Frequent Pattern Mining
Qian Wan and Aijun An
Department of Computer Science and Engineering
York University, Toronto, Ontario, M3J 1P3, Canada
Email: {qwan, aan}@cs.yorku.ca
Abstract-- Mining frequent patterns is one of the fundamental
and essential operations in many data mining applications, such
as discovering association rules. In this paper, we propose an
innovative approach to generating compact transaction databases
for efficient frequent pattern mining. It uses a compact tree
structure, called CT-tree, to compress the original transactional
data. This allows the CT-Apriori algorithm, which is revised from
the classical Apriori algorithm, to generate frequent patterns
quickly by skipping the initial database scan and reducing a great
amount of I/O time per database scan. Empirical evaluations
show that our approach is effective, efficient and promising,
while the storage space requirement as well as the mining time
can be decreased dramatically on both synthetic and real-world
databases.

  

Source: An, Aijun - Department of Computer Science, York University (Toronto)

 

Collections: Computer Technologies and Information Sciences