Top Down FPGrowth for Association Rule Mining

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Top Down FP-Growth for Association Rule Mining

• Ke Wang
• Liu Tang
• Jiawei Han
• Junqiang Liu
• Simon Fraser University

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Introduction

• Association rule A ? B
• A and B sets of items
• support count(AB ) ( of transaction containing
  AB)
• Frequent gt minimum support
• confidence count( AB ) / count(A )
• Confidentgt minimum confidence
• Input a set of transactions
• find all frequent patterns AB and A

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TD-FP-Growth for frequent pattern mining

• Similar prefix tree as FP-tree
• Items in transactions are sorted
• Transactions share prefix as much as possible
• FP-growth bottom-up mining
• TD-FP-Growth top-down mining

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FP-Growth Bottom-up minig
b, e a, b, c, e b, c, e a, c, d a minsup 2
Mining order e, c, b, a
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FP-Growth Bottom-up mining
? drawback!
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FP-Growth Top-down mining(TD-FP-Growth)

• process nodes at upper level first
• counts modified at upper level are not used at
  lower level
• reuse the paths in the original FP-tree for
  conditional pattern FP-trees

See example ?
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TD-FP-Growth
b, e a, b, c, e b, c, e a, c, d a minsup 2
Mining order a, b, c, e
CT-tree and header table H
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TD-FP-Growth
b, e a, b, c, e b, c, e a, c, d a minsup 2
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Performance

• Data sets from UC_Irvine Machine Learning
  Database Repository http//www.ics.uci.
  edu/mlearn/MLRepository.html.

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Performance