A Survey Paper on Sequence Pattern Mining with Incremental Approach
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].
To list your conference here. Please contact the administrator of this platform.
Paper submission email: CEIS@iiste.org
ISSN (Paper)2222-1727 ISSN (Online)2222-2863
Please add our address "contact@iiste.org" into your email contact list.
This journal follows ISO 9001 management standard and licensed under a Creative Commons Attribution 3.0 License.
Copyright © www.iiste.org