Skip to main content

An efficient approach for the maintenance of user behaviours

Buy Article:

$60.90 plus tax (Refund Policy)

Abstract:

Mining sequential patterns is to discover sequential purchasing behaviours for most of the customers from a large number of customer transactions. The strategy of mining sequential patterns focuses on discovering frequent sequences. A frequent sequence is an ordered list of the itemsets purchased by a sufficient number of customers. The previous approaches for mining sequential patterns need to repeatedly scan the database so that they take a large amount of computation time to find frequent sequences. The customer transactions will grow rapidly in a short time, and some of the customer transactions may be antiquated. Consequently, the frequent sequences may be changed due to the insertion of new customer transactions or the deletion of old customer transactions from the database. It may require rediscovering all the patterns by scanning the entire updated customer transaction database. In this paper, we propose an incremental updating technique to maintain the discovered sequential patterns when transactions are inserted into or deleted from the database. Our approach partitions the database into some segments and scans the database segment by segment. For each segment scan, our approach prunes those sequences that cannot be frequent sequences any more to accelerate the finding process of the frequent sequences. Therefore, the number of database scans can be significantly reduced by our approach. The experimental results show that our algorithms are more efficient than other algorithms for the maintenance of mining sequential patterns.

Keywords: data mining; frequent sequence; incremental updating; sequential pattern; transaction database

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01449290701820908

Affiliations: Department of Computer Science and Information Engineering, Ming Chuan University, Taipei, Taiwan

Publication date: November 1, 2009

More about this publication?
tandf/tbit/2009/00000028/00000006/art00004
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more