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Abstract: In this paper we present Cheese, a modular Generic Search framework, useful for implementing various tasks common in Data Mining. The main advantage of such a framework is the possibility for splitting and recombining search tasks or executing meta-search (in the space of search algorithms). (Update)
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BibTeX entry: (Update)
@misc{ ludl-cheese,
author = "Marcus-Christopher Ludl",
title = "Cheese: A Generic Search Framework for Data Mining",
url = "citeseer.ist.psu.edu/ludl02cheese.html" }
Citations (may not include all citations):233 The cn2 induction algorithm - Clark, Niblett - 1989
31 Opus: An efficient admissible algorithm for unordered search - Webb - 1995
13 Efficient search for association rules - Webb - 2000
11 Artificial Intelligence Review (context) - Furnkranz, learning - 1999
11 A local search template - Vaessens, Aarts et al. - 1992
4 Rule-space search for knowledge-based discovery - Provost - 1999
1 Data mining systems development (context) - Siebes
Documents on the same site (http://www.oefai.at/~marcus/publications.htm):
Towards a Simple Clustering Criterion Based on Minimum Length.. - Ludl, Widmer (2002) (Correct)
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