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Abstract: Because the knowledge discovery process is ill-defined, iterative, and requires intense interaction, algorithm flexibility is crucial. In this paper, we present a straighforward, heuristic generate-and-test search algorithm for knowledge discovery. An analysis of the literature shows that this basic algorithm underlies many of the systems that have had practical success in data mining and knowledge discovery over the past twenty years. We argue that this search algorithm has persevered because... (Update)
Context of citations to this paper: More
...branches. On one hand, a number of researchers explored techniques for identifying large numbers of classi cation rules [4, 8, 10, 12, 14, 16]. This work was distinguished by the removal of the objective of using the rules for classi cation and hence of the requirement...
...In [Web95] the authors provide detailed descriptions for efficient admissible search and dynamic search space reorderings. [Pro99] describe a generic heuristic generate and test rule space search algorithm (GAT) and argue that a wide variety of knowledge discovery...
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BibTeX entry: (Update)
F. Provost, J. Aronis, and B. Buchanan. Rule-space search for knowledge-based discovery. CIIO Working Paper IS 99-012, Stern School of Business, New York University, , NY, NY 10012, 1999. http://citeseer.ist.psu.edu/article/provost99rulespace.html More
@misc{ provost99rulespace,
author = "F. Provost and J. Aronis and B. Buchanan",
title = "Rule-space search for knowledge-based discovery",
note = "{CIIO} Working Paper IS 99-012, Stern School of Business, New York
University, NY, NY 10012.",
year = "1999",
url = "citeseer.ist.psu.edu/article/provost99rulespace.html" }
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Documents on the same site (http://www.stern.nyu.edu/~fprovost/Classes/rolling-readings-syllabus.html):
Multiple Comparisons in Induction Algorithms - Jensen, COHEN (1999) (Correct)
Robust Classification Analysis for Performance Evaluation - Provost, Fawcett (2001) (Correct)
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