02-07-2013, 04:22 PM
WEB USAGE MINING FRAMEWORK FOR MINING EVOLVING USER PROFILES IN DYNAMIC WEBSITE
WEB USAGE MINING.ppt (Size: 657 KB / Downloads: 61)
INTRODUCTION
PURPOSE:
we present a complete framework and findings
in mining Web usage patterns from Web log
files of a real Web site that has all the
challenging aspects of real-life.
SCOPE:
Web usage mining, includes evolving user
Profiles and external data describing ontology
of the Web content.
SYSTEM ANALYSIS
EXISTING SYSTEM:
The fast pace and large amounts of data available in these
online settings have recently made it imperative to use
automated data mining or knowledge discovery techniques to
discover Web user profiles.
PROPOSED SYSTEM:
The different modes of usage or the so-called mass user
profiles can be discovered using Web usage mining
techniques that can automatically extract frequent
access patterns from the history of previous user
clickstreams stored in Web log files.
MODULE DESCRIPTION
Administration:
Handling profile evolution. Integrating semantics in Web usage mining. Profile
discovery based on web usage. Pre- process web log file to extract user sessions.
Client:
Web access patterns on a Web site are dynamic due not only to the dynamics of
Web site content and structure but also to changes in the user’s interests and,
thus, their navigation patterns.
Retrieve dynamic info and static info:
This description is reminiscent of an information retrieval scenario in the sense that
profiles that are retrieved should be as close as possible to the original session data.
Report generation:
After filtering out irrelevant entries, the data was segmented into sessions based on
the client IP address and a time-out threshold between two consecutive accesses in
the same session of 45 minutes.
CONCLUSION & FUTURE WORK
CONCLUSION:
A multifaceted user profile summarizes a group of users with
similar access activities and consists of their viewed pages,
search engine queries and inquiring and inquired companies.
FUTURE WORK:
Web pages can be pre-fetched depending on the usage patterns.
Further, the method for analysing sparse data can be used in the study of Web log access.