A toolbox approach to flexible and efficient data mining

Ole M. Nielsen; Peter Christen; Markus Hegland; Tatiana Semenova; Timothy Hancock

doi:10.1007/3-540-45357-1_16

A toolbox approach to flexible and efficient data mining

Ole M. Nielsen^*, Peter Christen, Markus Hegland, Tatiana Semenova, Timothy Hancock

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

This paper describes a flexible and efficient toolbox based on the scripting language Python, capable of handling common tasks in data mining. Using either a relational database or flat files the toolbox gives the user a uniform view of a data collection. Two core features of the toolbox are caching of database queries and parallelism within a collection of independent queries. Our toolbox provides a number of routines for basic data mining tasks on top of which the user can add more functions - mainly domain and data collection dependent - for complex and time consuming data mining tasks.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings
Editors	David Cheung, Graham J. Williams, Qing Li
Publisher	Springer Verlag
Pages	124-135
Number of pages	12
ISBN (Print)	3540419101, 9783540419105
DOIs	https://doi.org/10.1007/3-540-45357-1_16
Publication status	Published - 2001
Event	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 - Kowloon, Hong Kong Duration: 16 Apr 2001 → 18 Apr 2001

Publication series

Name	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2035
ISSN (Print)	0302-9743

Conference

Conference	5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001
Country/Territory	Hong Kong
City	Kowloon
Period	16/04/01 → 18/04/01

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10.1007/3-540-45357-1_16

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Nielsen, O. M., Christen, P., Hegland, M., Semenova, T., & Hancock, T. (2001). A toolbox approach to flexible and efficient data mining. In D. Cheung, G. J. Williams, & Q. Li (Eds.), Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings (pp. 124-135). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2035). Springer Verlag. https://doi.org/10.1007/3-540-45357-1_16

Nielsen, Ole M. ; Christen, Peter ; Hegland, Markus et al. / A toolbox approach to flexible and efficient data mining. Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. editor / David Cheung ; Graham J. Williams ; Qing Li. Springer Verlag, 2001. pp. 124-135 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)).

@inproceedings{e605308361f141c1b42ec0c98ffeb240,

title = "A toolbox approach to flexible and efficient data mining",

abstract = "This paper describes a flexible and efficient toolbox based on the scripting language Python, capable of handling common tasks in data mining. Using either a relational database or flat files the toolbox gives the user a uniform view of a data collection. Two core features of the toolbox are caching of database queries and parallelism within a collection of independent queries. Our toolbox provides a number of routines for basic data mining tasks on top of which the user can add more functions - mainly domain and data collection dependent - for complex and time consuming data mining tasks.",

keywords = "Caching, Health data, Python, Relational database, SQL",

author = "Nielsen, {Ole M.} and Peter Christen and Markus Hegland and Tatiana Semenova and Timothy Hancock",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001 ; Conference date: 16-04-2001 Through 18-04-2001",

year = "2001",

doi = "10.1007/3-540-45357-1_16",

language = "English",

isbn = "3540419101",

series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

publisher = "Springer Verlag",

pages = "124--135",

editor = "David Cheung and Williams, {Graham J.} and Qing Li",

booktitle = "Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings",

address = "Germany",

}

Nielsen, OM, Christen, P , Hegland, M, Semenova, T & Hancock, T 2001, A toolbox approach to flexible and efficient data mining. in D Cheung, GJ Williams & Q Li (eds), Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 2035, Springer Verlag, pp. 124-135, 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001, Kowloon, Hong Kong, 16/04/01. https://doi.org/10.1007/3-540-45357-1_16

A toolbox approach to flexible and efficient data mining. / Nielsen, Ole M.; Christen, Peter ; Hegland, Markus et al.
Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. ed. / David Cheung; Graham J. Williams; Qing Li. Springer Verlag, 2001. p. 124-135 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2035).

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

TY - GEN

T1 - A toolbox approach to flexible and efficient data mining

AU - Nielsen, Ole M.

AU - Christen, Peter

AU - Hegland, Markus

AU - Semenova, Tatiana

AU - Hancock, Timothy

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2001.

PY - 2001

Y1 - 2001

N2 - This paper describes a flexible and efficient toolbox based on the scripting language Python, capable of handling common tasks in data mining. Using either a relational database or flat files the toolbox gives the user a uniform view of a data collection. Two core features of the toolbox are caching of database queries and parallelism within a collection of independent queries. Our toolbox provides a number of routines for basic data mining tasks on top of which the user can add more functions - mainly domain and data collection dependent - for complex and time consuming data mining tasks.

AB - This paper describes a flexible and efficient toolbox based on the scripting language Python, capable of handling common tasks in data mining. Using either a relational database or flat files the toolbox gives the user a uniform view of a data collection. Two core features of the toolbox are caching of database queries and parallelism within a collection of independent queries. Our toolbox provides a number of routines for basic data mining tasks on top of which the user can add more functions - mainly domain and data collection dependent - for complex and time consuming data mining tasks.

KW - Caching

KW - Health data

KW - Python

KW - Relational database

KW - SQL

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U2 - 10.1007/3-540-45357-1_16

DO - 10.1007/3-540-45357-1_16

M3 - Conference contribution

SN - 3540419101

SN - 9783540419105

T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

SP - 124

EP - 135

BT - Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings

A2 - Cheung, David

A2 - Williams, Graham J.

A2 - Li, Qing

PB - Springer Verlag

T2 - 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001

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Nielsen OM, Christen P , Hegland M, Semenova T, Hancock T. A toolbox approach to flexible and efficient data mining. In Cheung D, Williams GJ, Li Q, editors, Advances in Knowledge Discovery and Data Mining - 5th Pacific-Asia Conference, PAKDD 2001, Proceedings. Springer Verlag. 2001. p. 124-135. (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)). doi: 10.1007/3-540-45357-1_16

A toolbox approach to flexible and efficient data mining

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