Sequential Pattern Mining

Sequential pattern mining is a topic in data mining concerned with finding statistically relevant patterns among data examples whose values are delivered in a sequence. It addresses several related problems: building efficient databases and indexes for sequence data, extracting frequently occurring patterns, comparing sequences for similarity, and recovering missing members of a sequence. Two main approaches are used, string mining, which works over a limited alphabet across very long sequences such as DNA, protein or text, and itemset mining, which finds frequent items and their ordering, as in customer purchase histories or stock price movements. Common algorithms in the field include GSP, SPADE, PrefixSpan and FreeSpan, and applications range from retail and marketing, where the technique identifies buying patterns for shelf placement, to biology, where it helps analyze gene and protein sequences, to finance, where it detects patterns in stock price movements. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/