A survey of open source data mining systems

Xiaojun Chen; Yunming Ye; Graham Williams; Xiaofei Xu

doi:10.1007/978-3-540-77018-3_2

A survey of open source data mining systems

Xiaojun Chen
, Yunming Ye^*
, Graham Williams
, Xiaofei Xu

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Australian Taxation Office, Canberra

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

31 Scopus citations

Abstract

Open source data mining software represents a new trend in data mining research, education and industrial applications, especially in small and medium enterprises (SMEs). With open source software an enterprise can easily initiate a data mining project using the most current technology. Often the software is available at no cost, allowing the enterprise to instead focus on ensuring their staff can freely learn the data mining techniques and methods. Open source ensures that staff can understand exactly how the algorithms work by examining the source codes, if they so desire, and can also fine tune the algorithms to suit the specific purposes of the enterprise. However, diversity, instability, scalability and poor documentation can be major concerns in using open source data mining systems. In this paper, we survey open source data mining systems currently available on the Internet. We compare 12 open source systems against several aspects such as general characteristics, data source accessibility, data mining functionality, and usability. We discuss advantages and disadvantages of these open source data mining systems.

Original language	English
Title of host publication	Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers
Publisher	Springer Verlag
Pages	3-14
Number of pages	12
ISBN (Print)	354077016X, 9783540770169
DOIs	https://doi.org/10.1007/978-3-540-77018-3_2
State	Published - 2007
Externally published	Yes
Event	Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007 - Nanjing, China Duration: 22 May 2007 → 22 May 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4819 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
Country/Territory	China
City	Nanjing
Period	22/05/07 → 22/05/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth
SDG 9 Industry, Innovation, and Infrastructure

Keywords

Data mining
FLOSS
Open source software

Access to Document

10.1007/978-3-540-77018-3_2

Cite this

Chen, X., Ye, Y., Williams, G., & Xu, X. (2007). A survey of open source data mining systems. In Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers (pp. 3-14). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4819 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-77018-3_2

Chen, Xiaojun ; Ye, Yunming ; Williams, Graham et al. / A survey of open source data mining systems. Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers. Springer Verlag, 2007. pp. 3-14 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Chen, X, Ye, Y, Williams, G & Xu, X 2007, A survey of open source data mining systems. in Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4819 LNAI, Springer Verlag, pp. 3-14, Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007, Nanjing, China, 22/05/07. https://doi.org/10.1007/978-3-540-77018-3_2

A survey of open source data mining systems. / Chen, Xiaojun; Ye, Yunming; Williams, Graham et al.
Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers. Springer Verlag, 2007. p. 3-14 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4819 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ye, Yunming

AU - Williams, Graham

AU - Xu, Xiaofei

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N2 - Open source data mining software represents a new trend in data mining research, education and industrial applications, especially in small and medium enterprises (SMEs). With open source software an enterprise can easily initiate a data mining project using the most current technology. Often the software is available at no cost, allowing the enterprise to instead focus on ensuring their staff can freely learn the data mining techniques and methods. Open source ensures that staff can understand exactly how the algorithms work by examining the source codes, if they so desire, and can also fine tune the algorithms to suit the specific purposes of the enterprise. However, diversity, instability, scalability and poor documentation can be major concerns in using open source data mining systems. In this paper, we survey open source data mining systems currently available on the Internet. We compare 12 open source systems against several aspects such as general characteristics, data source accessibility, data mining functionality, and usability. We discuss advantages and disadvantages of these open source data mining systems.

AB - Open source data mining software represents a new trend in data mining research, education and industrial applications, especially in small and medium enterprises (SMEs). With open source software an enterprise can easily initiate a data mining project using the most current technology. Often the software is available at no cost, allowing the enterprise to instead focus on ensuring their staff can freely learn the data mining techniques and methods. Open source ensures that staff can understand exactly how the algorithms work by examining the source codes, if they so desire, and can also fine tune the algorithms to suit the specific purposes of the enterprise. However, diversity, instability, scalability and poor documentation can be major concerns in using open source data mining systems. In this paper, we survey open source data mining systems currently available on the Internet. We compare 12 open source systems against several aspects such as general characteristics, data source accessibility, data mining functionality, and usability. We discuss advantages and disadvantages of these open source data mining systems.

KW - Data mining

KW - FLOSS

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SN - 9783540770169

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

Chen X, Ye Y, Williams G, Xu X. A survey of open source data mining systems. In Emerging Technologies in Knowledge Discovery and Data Mining - PAKDD 2007 International Workshops, Revised Selected Papers. Springer Verlag. 2007. p. 3-14. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-77018-3_2

A survey of open source data mining systems

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this