|
Title:
|
CLUSTERING WEB PAGES SEQUENCES WITH ARTIFICIAL ANTS |
|
Author(s):
|
Nicolas Labroche |
|
ISBN:
|
ISSN: 1645-7641 |
|
Editors:
|
Pedro Isaías |
|
Year:
|
2007 |
|
Edition:
|
V V, 1 |
|
Keywords:
|
Web usage mining, clustering, similarity measure |
|
Type:
|
Journal Paper |
|
First Page:
|
45 |
|
Last Page:
|
59 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
This paper introduces new web usage mining tools designed to help characterizing user accesses on
websites. Our approach relies on a categorization of web user sessions to help identifying and
understanding major trends in the navigations. The novelty of our work mainly relies in the clustering
method that is a fast unsupervised ant-inspired clustering algorithm paired with new similarity measures
that handle sessions either as a sequence or an unordered set of web pages. Our algorithm is evaluated on
real web log files of a French museum that contains more than 39000 user sessions over one month and a
half. The evaluation is conducted according to the quality of the output partitions and the interpretability
of each cluster based on its content. Our experiments show that our algorithm can build meaningful
sessions clusters that can help infer Web users motivations. |
|
|
|
|
|
|
