Sequential pattern mining

Sequential pattern mining is a topic of <a href="/facts/Data_mining/AH53q5ac">data mining</a> concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence. It is usually presumed that the values are discrete, and thus <a href="/facts/Time_series/fSXPR817">time series</a> mining is closely related, but usually considered a different activity. Sequential pattern mining is a special case of <a href="/facts/Structured_data_mining/wTYpHwGA">structured data mining</a>.
There are several key traditional computational problems addressed within this field. These include building efficient databases and indexes for sequence information, extracting the frequently occurring patterns, comparing sequences for <a href="/facts/Similarity_measure/FmmFco8c">similarity</a>, and recovering missing sequence members. In general, sequence mining problems can be classified as string mining which is typically based on <a href="/facts/String_(computer_science)/4ChJu5WS">string processing algorithms</a> and itemset mining which is typically based on <a href="/facts/Association_rule_learning/oX6w9Kux">association rule learning</a>. Local process models extend sequential pattern mining to more complex patterns that can include (exclusive) choices, loops, and concurrency constructs in addition to the sequential ordering construct.

Sequential pattern mining open-in-new

Sequential pattern mining