Efficient Data Mining for Maximal Frequent Subtrees

  • Authors:
  • Yongqiao Xiao;Jenq-Foung Yao;Zhigang Li;Margaret H. Dunham

  • Affiliations:
  • -;-;-;-

  • Venue:
  • ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
  • Year:
  • 2003

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Abstract

A new type of tree mining is defined in this paper,which uncovers maximal frequent induced subtrees from adatabase of unordered labeled trees. A novel algorithm,PathJoin, is proposed. The algorithm uses a compact datastructure, FST-Forest, which compresses the trees and stillkeeps the original tree structure. PathJoin generates candidatesubtrees by joining the frequent paths in FST-Forest.Such candidate subtree generation is localized and thussubstantially reduces the number of candidate subtrees. Experimentswith synthetic data sets show that the algorithmis effective and efficient.