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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... (Update)
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.... Michalski et al.[76] and Morik[80] and data mining communities (see Craven and Shavlik[25] Glymour et al.[41] Fayyad and Smyth[36] and Fayyad et al.[35] feature selection has been studied widely in the pattern recognition and statistics literature. Reviews of this...
...primarily focuC3 onvisu#OqGW[3C techniquO that increase the e#ectiveness of the KDD process by means of sophisticatedu ser interaction. In [3], five case stu[3 are presented in order to bringu p criticalissua and to show the potential contribu3Wq of applying data mining...
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BibTeX entry: (Update)
Fayyad, U.; Haussler, D.; and Stolorz, P. 1996. KDD for science data analysis: Issues and examples. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 50--56. Menlo Park, CA: AAAI Press. http://citeseer.ist.psu.edu/article/fayyad96kdd.html More
@inproceedings{ fayyad96kdd,
author = "Usama M. Fayyad and David Haussler and Paul E. Stolorz",
title = "{KDD} for Science Data Analysis: Issues and Examples",
booktitle = "Knowledge Discovery and Data Mining",
pages = "50-56",
year = "1996",
url = "citeseer.ist.psu.edu/article/fayyad96kdd.html" }
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