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Interactive path analysis of web site traffic
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Source International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
San Francisco, California
Pages: 414 - 419  
Year of Publication: 2001
ISBN:1-58113-391-X
Authors
Pavel Berkhin  Accrue Software, Inc., Fremont, CA
Jonathan D. Beche  Accrue Software, Inc., Fremont, CA
Dee Jay Randall  Accrue Software, Inc., Fremont, CA
Sponsors
SIGMOD: ACM Special Interest Group on Management of Data
AAAI : American Association for Artificial Intelligence
Publisher
ACM  New York, NY, USA
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ABSTRACT

The goal of Path Analysis is to understand visitors' navigation of a Web site. The fundamental analysis component is a path. A path is a finite sequence of elements, typically representing URLs or groups of URLs. A full path is an abstraction of a visit or a session, which can contain attributes described below. Subpaths represent interesting subsequences of the full paths.Path Analysis provides user-configurable extraction, filtering, preprocessing, noise reduction, descriptive statistics and detailed analysis of three basic specific objects: elements, (sub)paths, and couples of elements. In each case, lists of frequent objects --- subject to particular filtering and sorting --- are available. We call the corresponding interactive tools Element, Path, and Couple Analyzers.We also allow in-depth exploration of individual elements, paths, and couples: Element Explorer investigates composition and convergence of traffic through an element and allows conditioning based on the number of preceding/succeeding steps. Path Explorer visualizes in and out flows of a path and attrition rate along the path. Couple Explorer presents distinct paths connecting couple elements, along with measures of their association and some additional statistics.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Accrue Software, Inc. http://www.accrue.com/ Products/Accrue_G2/g2_overview.html.
 
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B.Berendt, B.Mobasher, M.Spiliopoulou, and J.Wiltshire. Measuring the Accuracy of Sessionizers for Web Usage Analysis, A Summary of Results, Workshop on Web Mining, First SIAM Intl. Conf. On Data Mining, Chicago, 2001, 7-14.
 
5
D.Berndt and J.Clifford. Finding patterns in time series in advances. U.Fayyad et al (eds.). Knowledge Discovery and Data Mining, MIT Press, 37-59, 1996.
6
 
7
P.K.Chan. A non-invasive learning approach to building web user profiles, WebKDD-99 Workshop on Web Usage Analysis and User Profiling, 7-12, San Diego, 1999.
8
 
9
 
10
R.Cooley, B.Mobasher, and J.Srivastava. Web Mining: Information and Pattern Discovery on the World Wide Web, Department of Computer Science University of Minnesota Minneapolis, MN 55455, USA, 1997.
 
11
B.A.Huberman, P.Pirolli, J.Pitkow and R.J.Lukose. Strong Regularities in World Wide Web Surfing. Science 280, 95-97, 1998.
 
12
P.Pirolli and S.K.Card. Information Foraging. Psychological Review, 106(4), 643-675, 1999.
 
13
 
14
 
15
J.E.Pitkow and P.Pirolli. Mining longest repeated subsequences to predict World Wide Web surfing. Second USENIX Symposium on Internet Technologies and Systems, 1999.
 
16
M.A.Roytberg. A search for common patterns in many sequences. Computer Applications in the Biosciences, 8(1), 57-64, 1992.
 
17
 
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Collaborative Colleagues:
Pavel Berkhin: colleagues
Jonathan D. Beche: colleagues
Dee Jay Randall: colleagues