A Toolbox Approach to Flexible and Efficient Data Mining

Authors:
Ole Møller Nielsen;Peter Christen;Markus Hegland;Tatiana Semenova;Timothy Hancock
Affiliations:
-;-;-;-;-
Venue:
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Year:
2001

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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.