Efficient Algorithms for Finding Edit-Distance Based Motifs

Peng Xiao, Xingyu Cai, Sanguthevar Rajasekaran

Abstract: Motif mining is a classical data mining problem which aims to extract relevant information and discover knowledge from voluminous datasets in a variety of domains. Specifically, for the temporal data containing real numbers, it is formulated as time series motif mining (TSMM) problem. If the input is alphabetical and edit-distance is considered, this is called Edit-distance Motif Search (EMS). In EMS, the problem of interest is to find a pattern of length l which occurs with an edit-distance of at most d in each of the input sequences.