Affordable Access

Publisher Website

Efficient mining of frequent episodes from complex sequences

Authors
Journal
Information Systems
0306-4379
Publisher
Elsevier
Publication Date
Volume
33
Issue
1
Identifiers
DOI: 10.1016/j.is.2007.07.003
Keywords
  • Data Mining
  • Frequent Episodes
  • Temporal Association
Disciplines
  • Computer Science

Abstract

Abstract Discovering patterns with great significance is an important problem in data mining discipline. An episode is defined to be a partially ordered set of events for consecutive and fixed-time intervals in a sequence. Most of previous studies on episodes consider only frequent episodes in a sequence of events (called simple sequence). In real world, we may find a set of events at each time slot in terms of various intervals (hours, days, weeks, etc.). We refer to such sequences as complex sequences. Mining frequent episodes in complex sequences has more extensive applications than that in simple sequences. In this paper, we discuss the problem on mining frequent episodes in a complex sequence. We extend previous algorithm MINEPI to MINEPI + for episode mining from complex sequences. Furthermore, a memory-anchored algorithm called EMMA is introduced for the mining task. Experimental evaluation on both real-world and synthetic data sets shows that EMMA is more efficient than MINEPI + .

There are no comments yet on this publication. Be the first to share your thoughts.