The International Institute for Science, Technology and Education (IISTE)
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]
