or more sequences; examples of popular methods include BLAST for comparing a single sequence with multiple sequences in a database, and ClustalW for multiple alignments. Alignment algorithms can be based on either exact or approximate methods, and can also be classified as global alignments, semi-global alignments and local alignment. See sequence alignment. Itemset mining Some problems in sequence mining lend themselves to discovering frequent itemsets and the order they appear, for example, one is seeking rules of the form "if a {customer buys a car}, he or she is likely to {buy insurance} within 1 week", or in the context of stock prices, "if {Nokia up and Ericsson up}, it is likely that {Motorola up and Samsung up} within 2 days". Traditionally, itemset mining is used in marketing applications for discovering regularities between frequently co-occurring items in large transactions. For example, by analysing transactions of customer shopping baskets in a supermarket, one can produce a rule which reads "if a customer buys onions and potatoes together, he or she is likely to also buy hamburger meat in the same transaction". A survey and taxonomy of the key algorithms for item set mining is presented by Han et al. (2007). The two common techniques that are applied to sequence databases for frequent itemset mining are the influential apriori algorithm and the more-recent FP-growth technique. Applications With a great variation of products and user buying behaviors, shelf on which products are being displayed is one of the most important resources in retail environment. Retailers can not only increase their profit but, also decrease cost by proper management of shelf space allocation and products display. To solve this problem, George and Binu (2013) have proposed an approach to mine user buying patterns using PrefixSpan algorithm and place the products on shelves based on the order of mined purchasing patterns. Algorithms Commonly used algorithms include: GSP algorithm Sequential Pattern Discovery using Equivalence classes (SPADE) FreeSpan PrefixSpan MAPres Seq2Pat (for constraint-based sequential pattern mining) See also References External links SPMF includes open-source implementations of GSP, PrefixSpan, SPADE, SPAM and many others. Data mining Bioinformatics Bioinformatics algorithms