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» Mining Multiple Large Databases
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DS
1997
117views Database» more  DS 1997»
15 years 3 months ago
Experience with a Combined Approach to Attribute-Matching Across Heterogeneous Databases
Determining attribute correspondences is a difficult, time-consuming, knowledge-intensive part of database integration. We report on experiences with tools that identified candi...
Chris Clifton, E. Housman, Arnon Rosenthal
KDD
2003
ACM
148views Data Mining» more  KDD 2003»
16 years 2 months ago
Mining data records in Web pages
A large amount of information on the Web is contained in regularly structured objects, which we call data records. Such data records are important because they often present the e...
Bing Liu, Robert L. Grossman, Yanhong Zhai
KDD
1994
ACM
82views Data Mining» more  KDD 1994»
15 years 6 months ago
Architectural Support for Data Mining
Oneof the mainobstacles in applying data mining techniques to large, real-world databasesis the lack of efficient data management.In this paper, wepresent the design and implement...
Marcel Holsheimer, Martin L. Kersten
KDD
2002
ACM
127views Data Mining» more  KDD 2002»
16 years 2 months ago
Mining knowledge-sharing sites for viral marketing
Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mi...
Matthew Richardson, Pedro Domingos
KDD
2009
ACM
188views Data Mining» more  KDD 2009»
16 years 2 months ago
Mining discrete patterns via binary matrix factorization
Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rankone binary matrix approximations that identify the dominant patt...
Bao-Hong Shen, Shuiwang Ji, Jieping Ye