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GSP algorithm
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<p>GSP algorithm (<i>Generalized Sequential Pattern</i> algorithm) is an <a href="/facts/Algorithm/fnl5NmRt">algorithm</a> used for <a href="/facts/Sequence_mining/TW9SSJeo">sequence mining</a>. The algorithms for solving sequence mining problems are mostly based on the <i><a href="/facts/Apriori_algorithm/CNzfApBD">apriori</a></i> (level-wise) algorithm. One way to use the level-wise paradigm is to first discover all the frequent items in a level-wise fashion. It simply means counting the occurrences of all singleton elements in the database. Then, the <a href="/facts/Transaction_(database)/q1Etrsl8">transactions</a> are filtered by removing the non-frequent items. At the end of this step, each transaction consists of only the frequent elements it originally contained. This modified database becomes an input to the GSP algorithm. This process requires one pass over the whole <a href="/facts/Database/VYcpkevb">database</a>. </p><p>GSP algorithm makes multiple database passes. In the first pass, all single items (1-sequences) are counted. From the frequent items, a set of candidate 2-sequences are formed, and another pass is made to identify their frequency. The frequent 2-sequences are used to generate the candidate 3-sequences, and this process is repeated until no more frequent sequences are found. There are two main steps in the algorithm. </p> <ul><li>Candidate Generation. Given the set of frequent (k-1)-frequent sequences Fk-1, the candidates for the next pass are generated by joining F(k-1) with itself. A pruning phase eliminates any sequence, at least one of whose subsequences is not frequent.</li> <li>Support Counting. Normally, a <a href="/facts/Hash_tree_(persistent_data_structure)/YqxhuTTM">hash tree</a>–based search is employed for efficient support counting. Finally non-maximal frequent sequences are removed.</li></ul>