In this paper, we introduce a diamond episode of the form s1 -> E -> s2, where s1 and s2 are events and E is a set of events. The diamond episode s1 -> E -> s2 means that every event of E follows an event s1 and is followed by an event s2. Then, by formulating the support of diamond episodes, in this paper, we design the algorithm FreqDmd to extract all of the frequent diamond episodes from a given event sequence. Finally, by applying the algorithm FreqDmd to bacterial culture data,we extract diamond episodes representing replacement of bacteria.
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+.