[1][2] It is usually presumed that the values are discrete, and thus time series mining is closely related, but usually considered a different activity.
In biology applications analysis of the arrangement of the alphabet in strings can be used to examine gene and protein sequences to determine their properties.
A survey and taxonomy of the key algorithms for sequence comparison for bioinformatics is presented by Abouelhoda & Ghanem (2010), which include:[4] 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.
Retailers can not only increase their profit but, also decrease cost by proper management of shelf space allocation and products display.