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» Set Similarity Join on Probabilistic Data
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KDD
2001
ACM
253views Data Mining» more  KDD 2001»
14 years 7 months ago
GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces
The similarity join is an important operation for mining high-dimensional feature spaces. Given two data sets, the similarity join computes all tuples (x, y) that are within a dis...
Jens-Peter Dittrich, Bernhard Seeger
WWW
2004
ACM
14 years 8 months ago
Web data integration using approximate string join
Web data integration is an important preprocessing step for web mining. It is highly likely that several records on the web whose textual representations differ may represent the ...
Yingping Huang, Gregory R. Madey
DEXA
2006
Springer
151views Database» more  DEXA 2006»
13 years 9 months ago
An Incremental Refining Spatial Join Algorithm for Estimating Query Results in GIS
Geographic information systems (GIS) must support large georeferenced data sets. Due to the size of these data sets finding exact answers to spatial queries can be very time consum...
Wan D. Bae, Shayma Alkobaisi, Scott T. Leutenegger
SIGMOD
2007
ACM
192views Database» more  SIGMOD 2007»
14 years 7 months ago
Benchmarking declarative approximate selection predicates
Declarative data quality has been an active research topic. The fundamental principle behind a declarative approach to data quality is the use of declarative statements to realize...
Amit Chandel, Oktie Hassanzadeh, Nick Koudas, Moha...
UAI
2001
13 years 8 months ago
Similarity Measures on Preference Structures, Part II: Utility Functions
In previous work [8] we presented a casebased approach to eliciting and reasoning with preferences. A key issue in this approach is the definition of similarity between user prefe...
Vu A. Ha, Peter Haddawy, John Miyamoto