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Challenger USA Space Shuttle O-Ring Data Set

Below are papers that cite this data set, with context shown. Papers were automatically harvested and associated with this data set, in collaboration with

Return to Challenger USA Space Shuttle O-Ring data set page.

Stephen D. Bay. Multivariate Discretization for Set Mining. Knowl. Inf. Syst, 3. 2001.

that deals with the positioning of radiators in the Space Shuttle { UCI Admissions Data. This dataset represents all undergraduate student applications to UCI for the years 1993-1999. There are about 18000 applicants per year and the data contains variables such as ethnicity, UCI School (e.g. Arts,

Pedro Domingos. Linear-Time Rule Induction. KDD. 1996.

level of C4.5RULES's. In this paper we present CWS, a new algorithm with guaranteed O(e) complexity, and verify that it outperforms C4.5RULES and CN2 in time, accuracy and output size on two large datasets. For example, on NASA's space shuttle database, running time is reduced from over a month (for C4.5RULES) to a few hours, with a slight gain in accuracy. CWS is based on interleaving the induction

Mohammed Waleed Kadous and Claude Sammut. The University of New South Wales School of Computer Science and Engineering Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series.

analysis technique (i.e. a technique that allows the system to cope with the problem that patterns occur at different temporal scales) and applies them to space shuttle data as well as an artificial dataset. Mannila et al [MTV95] have also been looking at temporal classification problems, in particular applying it to network traffic analysis. In their model, streams are a sequence of time-labelled

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