Abstract
| Language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| Number of pages | 6 |
| Publication status | Accepted/In press - 15 May 1999 |
| Event | ACM Workshop on Web Usage Analysis and User Profiling (WebKDD) - San Diego, CA, USA Duration: 15 May 1999 → … |
Workshop
| Workshop | ACM Workshop on Web Usage Analysis and User Profiling (WebKDD) |
|---|---|
| Period | 15/05/99 → … |
Fingerprint
Keywords
- Data Mining
- Internet Data
- Navigation Pattern Discovery
Cite this
}
Navigation Pattern Discovery from Internet Data. / Buchner, AG; Baumgarten, Matthias; Anand, SS; Mulvenna, Maurice; Hughes, John.
Unknown Host Publication. 1999.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Navigation Pattern Discovery from Internet Data
AU - Buchner, AG
AU - Baumgarten, Matthias
AU - Anand, SS
AU - Mulvenna, Maurice
AU - Hughes, John
PY - 1999/5/15
Y1 - 1999/5/15
N2 - Electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximize their marketing effort. The discovery of marketing related navigation patterns requires the development of data mining algorithms capable of discovering sequential access patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible navigation templates that can specify navigational behavior, as network structures for the capture of web site topologies, in addition to concept hierarchies and syntactic constraints. Unlike existing approaches, field dependency has been implemented, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three different types of contained-in relationships are supported, which express different types of browsing behavior. The carried out experimental evaluation have shown promising results in terms of functionality as well as scalability.
AB - Electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximize their marketing effort. The discovery of marketing related navigation patterns requires the development of data mining algorithms capable of discovering sequential access patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible navigation templates that can specify navigational behavior, as network structures for the capture of web site topologies, in addition to concept hierarchies and syntactic constraints. Unlike existing approaches, field dependency has been implemented, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three different types of contained-in relationships are supported, which express different types of browsing behavior. The carried out experimental evaluation have shown promising results in terms of functionality as well as scalability.
KW - Data Mining
KW - Internet Data
KW - Navigation Pattern Discovery
M3 - Conference contribution
BT - Unknown Host Publication
ER -