A Survey Paper on Sequence Pattern Mining with Incremental Approach

Rutva Patel J.S. Dhobi

Abstract


Sequential pattern mining finds frequently occurring patterns ordered by time. The problem was first introduced by Agrawal and Srikant [1]. An example of a sequential pattern is “A customer who purchased a new Ford Explorer two years ago, is likely to respond favourably to a trade-in option now”. Let X be the clause “purchased a new Ford Explorer” and Y be the clause “responds favourably to a trade-in”. Then notice that the pattern XY above, is different from pattern YX which states that “A customer who responded favourably to a trade-in two years ago, will purchase a Ford Explorer now”. The order in which X and Y appear is important, and hence XY and YX are mined as two separate patterns.Sequential pattern mining is widely applicable since many types of data have a time component to them. For example, it can be used in the medical domain to help determine a correct diagnosis from the sequence of symptoms experienced; over customer data to help target repeat customers; and with web-log data to better structure a company’s website for easier access to the most popular links[2].


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