System Maintenance:
There may be intermittent impact on performance while updates are in progress. We apologize for the inconvenience.
By Topic

Data mining for path traversal patterns in a web environment

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ming-Syan Chen ; IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA ; Jong Soo Park ; Yu, P.S.

In this paper, we explore a new data mining capability which involved mining path traversal patterns in a distributed information providing environment like world-wide-web. First, we convert the original sequence of log data into a set of maximal forward references and filter out the effect of some backward references which are mainly made for ease of traveling. Second, we derive algorithms to determine the frequent traversal patterns, i.e., large reference sequences, from the maximal forward references obtained. Two algorithms are devised for determining large reference sequences: one is based on some hashing and pruning techniques, and the other is further improved with the option of determining large reference sequences in batch so as to reduce the number of database scans required. Performance of these two methods is comparatively analyzed

Published in:

Distributed Computing Systems, 1996., Proceedings of the 16th International Conference on

Date of Conference:

27-30 May 1996