Paper Info
Title
Mining time-gap sequential patterns
Abstract
Mining sequential patterns is to discover sequential purchasing behaviors for most of the customers from a large amount of customer transactions. An example of such a pattern is that most of the customers purchased item B after purchasing item A, and then they purchased item C after using item B. The manager can use this information to promote item B and item C when a customer purchased item A and item B, respectively. However, the manager cannot know what time the customers will need these products if we only discover the sequential patterns without any extra information. In this paper, we develop a new algorithm to discover not only the sequential patterns but also the time interval between any two items in the pattern. We call this information the time-gap sequential patterns. An example of time-gap sequential pattern is that most customers purchased item A, and then they bought item B after m to n days, and then after p to q days, they bought item C. When a customer bought item A, the information about item B can be sent to this customer after m to n days, that is, we can provide the product information in which the customer is interested on the appropriate date.
Year
DOI
Venue
2012
10.1007/978-3-642-31087-4_65
IEA/AIE
Keywords
Field
DocType
sequential pattern,item c,item a,item b,customer transaction,item c.,mining time-gap sequential pattern,item b.,sequential purchasing behavior,mining sequential pattern,time-gap sequential pattern
Data mining,Computer science,Purchasing,Database transaction,Database
Conference
Citations 
PageRank 
References 
3
0.41
8
Authors
2
Name
Order
Citations
PageRank
Show-Jane Yen1537130.05
Yue-Shi Lee254341.14