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» A New Autocalibration Algorithm: Experimental Evaluation
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SIGMOD
2008
ACM
164views Database» more  SIGMOD 2008»
16 years 2 months ago
Finding frequent items in probabilistic data
Computing statistical information on probabilistic data has attracted a lot of attention recently, as the data generated from a wide range of data sources are inherently fuzzy or ...
Qin Zhang, Feifei Li, Ke Yi
CIKM
2009
Springer
15 years 8 months ago
Reducing the risk of query expansion via robust constrained optimization
We introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion framework in which model estimation is cast as a r...
Kevyn Collins-Thompson
114
Voted
KDD
2010
ACM
208views Data Mining» more  KDD 2010»
15 years 9 days ago
Towards mobility-based clustering
Identifying hot spots of moving vehicles in an urban area is essential to many smart city applications. The practical research on hot spots in smart city presents many unique feat...
Siyuan Liu, Yunhuai Liu, Lionel M. Ni, Jianping Fa...
KDD
2008
ACM
239views Data Mining» more  KDD 2008»
16 years 2 months ago
Mining adaptively frequent closed unlabeled rooted trees in data streams
Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. We propose a new approach for mining closed unlabeled rooted trees a...
Albert Bifet, Ricard Gavaldà
KDD
2007
ACM
159views Data Mining» more  KDD 2007»
16 years 2 months ago
Constraint-driven clustering
Clustering methods can be either data-driven or need-driven. Data-driven methods intend to discover the true structure of the underlying data while need-driven methods aims at org...
Rong Ge, Martin Ester, Wen Jin, Ian Davidson