Visualizing and Classifying the Pattern of User’s Browsing Behavior for Website Design Recommendation (2004) [3 citations — 3 self]
Download:
http://www-users.cs.york.ac.uk/~derrick/document/p
http://www-users.cs.york.ac.uk/~kimble/research/Do
CACHED:
http://www-users.cs.york.ac.uk/~derrick/document/p
http://www-users.cs.york.ac.uk/~kimble/research/Do
CACHED:
by
I-hsien Ting
,
Chris Kimble
,
Daniel Kudenko
Proceedings of First International Workshop on Knowledge Discovery in Data Stream (ECML/PKDD ‘04
Add To MetaCart
BibTeX | Add To MetaCart
@INPROCEEDINGS{Ting04visualizingand,
author = {I-hsien Ting and Chris Kimble and Daniel Kudenko},
title = {Visualizing and Classifying the Pattern of User’s Browsing Behavior for Website Design Recommendation},
booktitle = {Proceedings of First International Workshop on Knowledge Discovery in Data Stream (ECML/PKDD ‘04},
year = {2004},
pages = {101--102},
publisher = {}
}
author = {I-hsien Ting and Chris Kimble and Daniel Kudenko},
title = {Visualizing and Classifying the Pattern of User’s Browsing Behavior for Website Design Recommendation},
booktitle = {Proceedings of First International Workshop on Knowledge Discovery in Data Stream (ECML/PKDD ‘04},
year = {2004},
pages = {101--102},
publisher = {}
}
Bookmarks
OpenURL
Abstract:
Clickstream data from e-commerce customers is a principal resource for evaluation of a website design. Many tools have been proposed for the visualization of Clickstream data [1, 2]. However, they do neither point to website design weaknesses nor translate into the improvements. Usually, an expert needs to go through an often lengthy and
