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Science in the ArchivesPasts, Presents, Futures$
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Lorraine Daston

Print publication date: 2017

Print ISBN-13: 9780226432229

Published to Chicago Scholarship Online: September 2017

DOI: 10.7208/chicago/9780226432533.001.0001

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Querying the Archive: Data Mining from Apriori to PageRank

Querying the Archive: Data Mining from Apriori to PageRank

Chapter:
(p.311) Twelve Querying the Archive: Data Mining from Apriori to PageRank
Source:
Science in the Archives
Author(s):

Matthew L. Jones

Publisher:
University of Chicago Press
DOI:10.7208/chicago/9780226432533.003.0012

Focused on activity at Stanford, Google, and IBM, this chapter stresses the centrality of the database community of academic computer science and industry in the creation of data mining practices during the 1990s and early 2000s. Whether creating new search algorithms or tools for enhancing marketing, database practitioners could never forget the scale of data, understood not as something intangible but as something physical existing on slow hard drives, something incapable of being resident in memory, something requiring time to move from place to place and from drives to processors. The early creators of data mining offered powerful technological determinist narratives holding that great volumes of data require the development of new algorithms, the loosening of traditional accounts of statistical rigor, the creation of new epistemic virtues, and the creation of new experts. The sheer scale of data was held to demand—and to justify—new forms of scientific knowledge, at times in conflict with long-held views of statistical rigor.

Keywords:   data mining, database, statistical rigor, corporate archives, internet, search, marketing, apriori algorithm, page-rank, Google

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