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KDD
2009
ACM
611views Data Mining» more  KDD 2009»
16 years 6 months ago
Fast approximate spectral clustering
Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-s...
Donghui Yan, Ling Huang, Michael I. Jordan
KDD
2005
ACM
104views Data Mining» more  KDD 2005»
16 years 5 months ago
A hit-miss model for duplicate detection in the WHO drug safety database
The WHO Collaborating Centre for International Drug Monitoring in Uppsala, Sweden, maintains and analyses the world's largest database of reports on suspected adverse drug re...
Andrew Bate, G. Niklas Norén, Roland Orre
ACMICEC
2007
ACM
168views ECommerce» more  ACMICEC 2007»
15 years 9 months ago
Designing novel review ranking systems: predicting the usefulness and impact of reviews
With the rapid growth of the Internet, users' ability to publish content has created active electronic communities that provide a wealth of product information. Consumers nat...
Anindya Ghose, Panagiotis G. Ipeirotis

Publication
200views
14 years 1 months ago
Learning Tags from Unsegmented Videos of Multiple Human Actions
Providing methods to support semantic interaction with growing volumes of video data is an increasingly important challenge for data mining. To this end, there has been some succes...
Timothy Hospedales, Shaogang Gong, Tao Xiang
PAKDD
2009
ACM
209views Data Mining» more  PAKDD 2009»
16 years 2 months ago
Approximate Spectral Clustering.
Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-...
Christopher Leckie, James C. Bezdek, Kotagiri Rama...