MINING CONDENSED SETS OF FREQUENT EPISODES WITH MORE ACCURATE FREQUENCIES FROM COMPLEX SEQUENCES
Min Gan, Honghua Dai · International journal of innovative computing, information & control · 2012
Many previous approaches to frequent episode discovery only accept sim- ple sequences. Although a recent approach has been able tond frequent episodes from complex sequences, the discovered sets are neither condensed nor accurate. This paper investigates the discovery of condensed sets of frequent episodes from complex sequences. We adopt a novel anti-monotonic frequency measure based on non-redundant occurrences, and dene a condensed set, nDaCF (the set of non-derivable approximately closed fre- quent episodes) within a given maximal error bound of support. We then introduce a series of effective pruning strategies, and develop a method, nDaCF - Miner, for discov- ering nDaCF sets. Experimental results show that, when the error bound is somewhat high, the discovered nDaCF sets are two orders of magnitude smaller than complete sets, and nDaCF-miner is more efficient than previous mining approaches. In addition, the nDaCF sets are more accurate than the sets found by previous approaches. Keywords: Frequent episodes, Condensed sets, Sequence data mining