Navigation Pattern Discovery from Internet Data

AG Buchner, Matthias Baumgarten, SS Anand, Maurice Mulvenna, John Hughes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages6
Publication statusAccepted/In press - 15 May 1999
EventACM Workshop on Web Usage Analysis and User Profiling (WebKDD) - San Diego, CA, USA
Duration: 15 May 1999 → …

Workshop

WorkshopACM Workshop on Web Usage Analysis and User Profiling (WebKDD)
Period15/05/99 → …

Fingerprint

Websites
Marketing
Navigation
Internet
Electronic commerce
Syntactics
Data mining
Scalability
Topology

Keywords

  • Data Mining
  • Internet Data
  • Navigation Pattern Discovery

Cite this

Buchner, AG., Baumgarten, M., Anand, SS., Mulvenna, M., & Hughes, J. (Accepted/In press). Navigation Pattern Discovery from Internet Data. In Unknown Host Publication
Buchner, AG ; Baumgarten, Matthias ; Anand, SS ; Mulvenna, Maurice ; Hughes, John. / Navigation Pattern Discovery from Internet Data. Unknown Host Publication. 1999.
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abstract = "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.",
keywords = "Data Mining, Internet Data, Navigation Pattern Discovery",
author = "AG Buchner and Matthias Baumgarten and SS Anand and Maurice Mulvenna and John Hughes",
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Buchner, AG, Baumgarten, M, Anand, SS, Mulvenna, M & Hughes, J 1999, Navigation Pattern Discovery from Internet Data. in Unknown Host Publication. ACM Workshop on Web Usage Analysis and User Profiling (WebKDD), 15/05/99.

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 proceedingConference contribution

TY - GEN

T1 - Navigation Pattern Discovery from Internet Data

AU - Buchner, AG

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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

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BT - Unknown Host Publication

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Buchner AG, Baumgarten M, Anand SS, Mulvenna M, Hughes J. Navigation Pattern Discovery from Internet Data. In Unknown Host Publication. 1999