Database Reference
In-Depth Information
Chapter 10
Big Data Frequent Pattern Mining
David C. Anastasiu, Jeremy Iverson, Shaden Smith and George Karypis
Abstract Frequent pattern mining is an essential data mining task, with a goal
of discovering knowledge in the form of repeated patterns. Many efficient pattern
mining algorithms have been discovered in the last two decades, yet most do not
scale to the type of data we are presented with today, the so-called “Big Data”.
Scalable parallel algorithms hold the key to solving the problem in this context. In
this chapter, we review recent advances in parallel frequent pattern mining, analyzing
them through the Big Data lens. We identify three areas as challenges to designing
parallel frequent pattern mining algorithms: memory scalability, work partitioning,
and load balancing. With these challenges as a frame of reference, we extract and
describe key algorithmic design patterns from the wealth of research conducted in
this domain.
Keywords Data mining
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Parallel algorithms
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Frequent pattern mining
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Frequent
sequence mining
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Frequent graph mining
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Motif discovery
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Memory scalability
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Work partitioning
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Load balancing
1
Introduction
As an essential data mining task, frequent pattern mining has applications ranging
from intrusion detection and market basket analysis, to credit card fraud prevention
and drug discovery. Many efficient pattern mining algorithms have been discovered
in the last two decades, yet most do not scale to the type of data we are presented with
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