A mysterious drowning leads Audrey and Nathan into a dangerous community where children seem to be disappearing. Watch HD Episode: Mirror [videobb] Watch HD Episode: Mirror [videozer] Watch HD Episode: Mirror [vidbux-flv] Watch HD Episode: Mirror [vi...
Jack faces a showdown with a man he thought was long since dead. But while Rex takes extreme action, is it too late to prevent the collapse of society? Watch HD Episode: Mirror [videobb] Watch HD Episode: Mirror [videozer] Watch HD Episode: Mirror [vidbux-flv] Watch HD Episode: Mirror [vidxden-divx] Watch HD Episode: Mirror [videoweed] Watch [...]
Aaron discovers that a girl he mentored in junior high has become a successful porn star and feels he influenced her poorly. Back at the hospital, a doctor Sarah admires falls for Riley and bribes Sarah with great responsibilities at work in exchange for help impressing Riley. Elsewhere, Fitz and Ben visit a friend in [...]
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+.
In this paper, we introduce the class of k-partite episodes, which are time-series patterns of the form ?A1, . . . ,Ak? for sets Ai (1 = i = k) of events meaning that, in an input event sequence, every event of Ai is followed by every event of Ai+1 for every 1 = i < k. Then, we present a backtracking algorithm Kpar and its modification Kpar2 that find all of the frequent k-partite episodes from an input event sequence without duplication. By theoretical analysis, we show that these two algorithms run in polynomial delay and polynomial space in total input size.
N. Méger, and C. Rigotti. Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’04, page 313--324. Springer, (2004)