Sequential PAttern mining using a bitmap representation

  • Authors:
  • Jay Ayres;Jason Flannick;Johannes Gehrke;Tomi Yiu

  • Affiliations:
  • Cornell University;Cornell University;Cornell University;Cornell University

  • Venue:
  • Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2002

Quantified Score

Hi-index 0.01

Visualization

Abstract

We introduce a new algorithm for mining sequential patterns. Our algorithm is especially efficient when the sequential patterns in the database are very long. We introduce a novel depth-first search strategy that integrates a depth-first traversal of the search space with effective pruning mechanisms.Our implementation of the search strategy combines a vertical bitmap representation of the database with efficient support counting. A salient feature of our algorithm is that it incrementally outputs new frequent itemsets in an online fashion.In a thorough experimental evaluation of our algorithm on standard benchmark data from the literature, our algorithm outperforms previous work up to an order of magnitude.