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» Incremental Mining of Sequential Patterns in Large Databases
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EDBT
2006
ACM
113views Database» more  EDBT 2006»
13 years 9 months ago
Deferred Maintenance of Disk-Based Random Samples
Random sampling is a well-known technique for approximate processing of large datasets. We introduce a set of algorithms for incremental maintenance of large random samples on seco...
Rainer Gemulla, Wolfgang Lehner
SSDBM
2010
IEEE
112views Database» more  SSDBM 2010»
13 years 12 months ago
BEMC: A Searchable, Compressed Representation for Large Seismic Wavefields
Abstract. State-of-the-art numerical solvers in Earth Sciences produce multi terabyte datasets per execution. Operating on increasingly larger datasets becomes challenging due to i...
Julio López, Leonardo Ramírez-Guzm&a...
MM
2004
ACM
248views Multimedia» more  MM 2004»
14 years 1 months ago
Incremental semi-supervised subspace learning for image retrieval
Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These tech...
Xiaofei He
KDD
2006
ACM
164views Data Mining» more  KDD 2006»
14 years 8 months ago
Sampling from large graphs
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.)...
Jure Leskovec, Christos Faloutsos
KDD
2007
ACM
191views Data Mining» more  KDD 2007»
14 years 8 months ago
Modeling relationships at multiple scales to improve accuracy of large recommender systems
The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past useritem relationships. In this work, we propose no...
Robert M. Bell, Yehuda Koren, Chris Volinsky