Mining local data sources for learning global cluster models

Chak Man Lam; Xiao Feng Zhang; Kwok Wai CHEUNG

doi:10.1109/WI.2004.10044

Mining local data sources for learning global cluster models

Chak Man Lam^*, Xiao Feng Zhang, Kwok Wai CHEUNG

^*Corresponding author for this work

Department of Computer Science

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

4 Citations (Scopus)

Abstract

Distributed data mining has been a topic getting more important nowadays as there are many cases where physically sharing of data is probibited, e.g., due to huge data volume or data privacy. In this paper, we are interested in learning a global cluster model by exploring data in distributed sources. A methodology based on periodic model exchange and merge is proposed and applied to hyperlinked Web pages analysis. In addition, we have tested a number of variations of the basic idea, including putting more emphasis on the privacy concern and testing the effect of having different numbers of distributed sources. Experimental results show that the proposed distributed learning scheme is effective with accuracy close to the case with all the data physically shared for the learning.

Original language	English
Title of host publication	Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004
Editors	N. Zhong, H. Tirri, Y. Yao, L. Zhou
Publisher	IEEE
Pages	748-751
Number of pages	4
ISBN (Print)	9780769521008, 0769521002
DOIs	https://doi.org/10.1109/WI.2004.10044
Publication status	Published - Sept 2004
Event	IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004 - Beijing, China Duration: 20 Sept 2004 → 24 Sept 2004

Publication series

Name	Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004

Conference

Conference	IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004
Country/Territory	China
City	Beijing
Period	20/09/04 → 24/09/04

Scopus Subject Areas

General Engineering

User-Defined Keywords

Data mining
Machine learning
Data analysis
Data privacy
Web pages
Testing
Frequency
Computer science
Machine learning algorithms
Training data

Access to Document

10.1109/WI.2004.10044

Cite this

Lam, C. M., Zhang, X. F., & CHEUNG, K. W. (2004). Mining local data sources for learning global cluster models. In N. Zhong, H. Tirri, Y. Yao, & L. Zhou (Eds.), Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004 (pp. 748-751). (Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004). IEEE. https://doi.org/10.1109/WI.2004.10044

@inproceedings{b67fa398040b41fe8cdbfd43f394faa4,

title = "Mining local data sources for learning global cluster models",

abstract = "Distributed data mining has been a topic getting more important nowadays as there are many cases where physically sharing of data is probibited, e.g., due to huge data volume or data privacy. In this paper, we are interested in learning a global cluster model by exploring data in distributed sources. A methodology based on periodic model exchange and merge is proposed and applied to hyperlinked Web pages analysis. In addition, we have tested a number of variations of the basic idea, including putting more emphasis on the privacy concern and testing the effect of having different numbers of distributed sources. Experimental results show that the proposed distributed learning scheme is effective with accuracy close to the case with all the data physically shared for the learning.",

keywords = "Data mining, Machine learning, Data analysis, Data privacy, Web pages, Testing, Frequency, Computer science, Machine learning algorithms, Training data",

author = "Lam, {Chak Man} and Zhang, {Xiao Feng} and CHEUNG, {Kwok Wai}",

year = "2004",

month = sep,

doi = "10.1109/WI.2004.10044",

language = "English",

isbn = "9780769521008",

series = "Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004",

publisher = "IEEE",

pages = "748--751",

editor = "N. Zhong and H. Tirri and Y. Yao and L. Zhou",

booktitle = "Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004",

address = "United States",

}

Lam, CM, Zhang, XF & CHEUNG, KW 2004, Mining local data sources for learning global cluster models. in N Zhong, H Tirri, Y Yao & L Zhou (eds), Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004. Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004, IEEE, pp. 748-751, IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004, Beijing, China, 20/09/04. https://doi.org/10.1109/WI.2004.10044

Mining local data sources for learning global cluster models. / Lam, Chak Man; Zhang, Xiao Feng; CHEUNG, Kwok Wai.
Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004. ed. / N. Zhong; H. Tirri; Y. Yao; L. Zhou. IEEE, 2004. p. 748-751 (Proceedings - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2004).

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

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AU - Zhang, Xiao Feng

AU - CHEUNG, Kwok Wai

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N2 - Distributed data mining has been a topic getting more important nowadays as there are many cases where physically sharing of data is probibited, e.g., due to huge data volume or data privacy. In this paper, we are interested in learning a global cluster model by exploring data in distributed sources. A methodology based on periodic model exchange and merge is proposed and applied to hyperlinked Web pages analysis. In addition, we have tested a number of variations of the basic idea, including putting more emphasis on the privacy concern and testing the effect of having different numbers of distributed sources. Experimental results show that the proposed distributed learning scheme is effective with accuracy close to the case with all the data physically shared for the learning.

AB - Distributed data mining has been a topic getting more important nowadays as there are many cases where physically sharing of data is probibited, e.g., due to huge data volume or data privacy. In this paper, we are interested in learning a global cluster model by exploring data in distributed sources. A methodology based on periodic model exchange and merge is proposed and applied to hyperlinked Web pages analysis. In addition, we have tested a number of variations of the basic idea, including putting more emphasis on the privacy concern and testing the effect of having different numbers of distributed sources. Experimental results show that the proposed distributed learning scheme is effective with accuracy close to the case with all the data physically shared for the learning.

KW - Data mining

KW - Machine learning

KW - Data analysis

KW - Data privacy

KW - Web pages

KW - Testing

KW - Frequency

KW - Computer science

KW - Machine learning algorithms

KW - Training data

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Mining local data sources for learning global cluster models

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

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