Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models

Qiming Huang; Qiang Yang; Joshua Zhexue Huang; Michael K. Ng

doi:10.1007/978-3-540-24775-3_65

Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models

Qiming Huang, Qiang Yang, Joshua Zhexue Huang, Michael K. Ng

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

7 Citations (Scopus)

Abstract

This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the total number of visits of a given Web page by all incoming users at the same time. The prediction technique can be used as a component for many Web based applications. The prediction model regards a Web browsing session as a continuous-time Markov process where the transition probability matrix can be computed from Web log data using the Kolmogorov’s backward equations. The model is tested against real Web-log data where the scalability and accuracy of our method are analyzed.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Subtitle of host publication	8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings
Editors	Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang
Publisher	Springer Berlin Heidelberg
Pages	549-558
Number of pages	10
Edition	1st
ISBN (Electronic)	9783540247753
ISBN (Print)	354022064X, 9783540220640
DOIs	https://doi.org/10.1007/978-3-540-24775-3_65
Publication status	Published - 22 Apr 2004
Event	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia Duration: 26 May 2004 → 28 May 2004 https://link.springer.com/book/10.1007/b97861 (Conference proceedings)

Publication series

Name	Lecture Notes in Computer Science
Volume	3056
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Lecture Notes in Artificial Intelligence
ISSN (Print)	2945-9133
ISSN (Electronic)	2945-9141

Name	PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining

Conference

Conference	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
Abbreviated title	PAKDD 2004
Country/Territory	Australia
City	Sydney
Period	26/05/04 → 28/05/04
Internet address	https://link.springer.com/book/10.1007/b97861 (Conference proceedings)

Scopus Subject Areas

Theoretical Computer Science
General Computer Science

User-Defined Keywords

Continuous time markov chain
Kolmogorov’s backward equations
Sessions
Transition probability
Web mining

Access to Document

10.1007/978-3-540-24775-3_65

Cite this

Huang, Q., Yang, Q., Huang, J. Z., & Ng, M. K. (2004). Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models. In H. Dai, R. Srikant, & C. Zhang (Eds.), Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings (1st ed., pp. 549-558). (Lecture Notes in Computer Science; Vol. 3056), (Lecture Notes in Artificial Intelligence), (PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_65

Huang, Qiming ; Yang, Qiang ; Huang, Joshua Zhexue et al. / Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models. Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings. editor / Honghua Dai ; Ramakrishnan Srikant ; Chengqi Zhang. 1st. ed. Springer Berlin Heidelberg, 2004. pp. 549-558 (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). (PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining).

@inproceedings{c5f03f3ed28a44b79dea22ba89e97758,

title = "Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models",

abstract = "This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the total number of visits of a given Web page by all incoming users at the same time. The prediction technique can be used as a component for many Web based applications. The prediction model regards a Web browsing session as a continuous-time Markov process where the transition probability matrix can be computed from Web log data using the Kolmogorov{\textquoteright}s backward equations. The model is tested against real Web-log data where the scalability and accuracy of our method are analyzed.",

keywords = "Continuous time markov chain, Kolmogorov{\textquoteright}s backward equations, Sessions, Transition probability, Web mining",

author = "Qiming Huang and Qiang Yang and Huang, {Joshua Zhexue} and Ng, {Michael K.}",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2004; 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004, PAKDD 2004 ; Conference date: 26-05-2004 Through 28-05-2004",

year = "2004",

month = apr,

day = "22",

doi = "10.1007/978-3-540-24775-3_65",

language = "English",

isbn = "354022064X",

series = "Lecture Notes in Computer Science",

publisher = "Springer Berlin Heidelberg",

pages = "549--558",

editor = "Honghua Dai and Ramakrishnan Srikant and Chengqi Zhang",

booktitle = "Advances in Knowledge Discovery and Data Mining",

edition = "1st",

url = "https://link.springer.com/book/10.1007/b97861",

}

Huang, Q, Yang, Q, Huang, JZ & Ng, MK 2004, Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models. in H Dai, R Srikant & C Zhang (eds), Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings. 1st edn, Lecture Notes in Computer Science, vol. 3056, Lecture Notes in Artificial Intelligence, PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer Berlin Heidelberg, pp. 549-558, 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004, Sydney, Australia, 26/05/04. https://doi.org/10.1007/978-3-540-24775-3_65

Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models. / Huang, Qiming; Yang, Qiang; Huang, Joshua Zhexue et al.
Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings. ed. / Honghua Dai; Ramakrishnan Srikant; Chengqi Zhang. 1st. ed. Springer Berlin Heidelberg, 2004. p. 549-558 (Lecture Notes in Computer Science; Vol. 3056), (Lecture Notes in Artificial Intelligence), (PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models

AU - Huang, Qiming

AU - Yang, Qiang

AU - Huang, Joshua Zhexue

AU - Ng, Michael K.

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2004

PY - 2004/4/22

Y1 - 2004/4/22

N2 - This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the total number of visits of a given Web page by all incoming users at the same time. The prediction technique can be used as a component for many Web based applications. The prediction model regards a Web browsing session as a continuous-time Markov process where the transition probability matrix can be computed from Web log data using the Kolmogorov’s backward equations. The model is tested against real Web-log data where the scalability and accuracy of our method are analyzed.

AB - This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the total number of visits of a given Web page by all incoming users at the same time. The prediction technique can be used as a component for many Web based applications. The prediction model regards a Web browsing session as a continuous-time Markov process where the transition probability matrix can be computed from Web log data using the Kolmogorov’s backward equations. The model is tested against real Web-log data where the scalability and accuracy of our method are analyzed.

KW - Continuous time markov chain

KW - Kolmogorov’s backward equations

KW - Sessions

KW - Transition probability

KW - Web mining

UR - http://www.scopus.com/inward/record.url?scp=7444220174&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-24775-3_65

DO - 10.1007/978-3-540-24775-3_65

M3 - Conference proceeding

AN - SCOPUS:7444220174

SN - 354022064X

SN - 9783540220640

T3 - Lecture Notes in Computer Science

SP - 549

EP - 558

BT - Advances in Knowledge Discovery and Data Mining

A2 - Dai, Honghua

A2 - Srikant, Ramakrishnan

A2 - Zhang, Chengqi

PB - Springer Berlin Heidelberg

T2 - 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004

Y2 - 26 May 2004 through 28 May 2004

ER -

Huang Q, Yang Q, Huang JZ, Ng MK. Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models. In Dai H, Srikant R, Zhang C, editors, Advances in Knowledge Discovery and Data Mining: 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings. 1st ed. Springer Berlin Heidelberg. 2004. p. 549-558. (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). (PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining). doi: 10.1007/978-3-540-24775-3_65

Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

Access to Document

Other files and links

Fingerprint

Cite this