KDD for Science Data Analysis: Issues and Examples (1996)
| Venue: | IN PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING |
| Citations: | 33 - 2 self |
BibTeX
@INPROCEEDINGS{Fayyad96kddfor,
author = {Usama Fayyad and David Haussler and Paul Stolorz},
title = {KDD for Science Data Analysis: Issues and Examples},
booktitle = {IN PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING},
year = {1996},
pages = {50--56},
publisher = {AAAI Press}
}
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Abstract
The analysis of the massive data sets collected by scientific instruments demands automation as a pre-requisite to analysis. There is an urgent need to create an intermediate level at which scientists can operate effectively; isolating them from the massive sizes and harnessing human analysis capabilities to focus on tasks in which machines do not even remotely approach humans---namely, creative data analysis, theory and hypothesis formation, and drawing insights into underlying phenomena. We give an overview of the main issues in the exploitation of scientific datasets, present five case studies where KDD tools play important and enabling roles, and conclude with future challenges for data mining and KDD techniques in science data analysis.
