Recently, knowledge discovery in large data increases its importance in various fields. Especially, data mining from time-series data gains much attention. This paper studies the problem of finding frequent episodes appearing in a sequence of events. We propose an efficient depth-first search algorithm for mining frequent serial episodes in a given event sequence using the notion of right-minimal occurrences. Then, we present some techniques for speeding up the algorithm, namely, occurrence-deliver and tail-redundancy pruning. Finally, we ran experiments on real datasets to evaluate the usefulness of the proposed methods.
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)