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» Large Scale Data Mining: Challenges and Responses
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SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
16 years 6 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
WWW
2009
ACM
16 years 6 months ago
Mapping the world's photos
We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analy...
David J. Crandall, Lars Backstrom, Daniel P. Hutte...
KDD
2009
ACM
347views Data Mining» more  KDD 2009»
16 years 6 months ago
Network anomaly detection based on Eigen equation compression
This paper addresses the issue of unsupervised network anomaly detection. In recent years, networks have played more and more critical roles. Since their outages cause serious eco...
Shunsuke Hirose, Kenji Yamanishi, Takayuki Nakata,...
SIGMOD
2004
ACM
140views Database» more  SIGMOD 2004»
16 years 6 months ago
Incremental and Effective Data Summarization for Dynamic Hierarchical Clustering
Mining informative patterns from very large, dynamically changing databases poses numerous interesting challenges. Data summarizations (e.g., data bubbles) have been proposed to c...
Corrine Cheng, Jörg Sander, Samer Nassar
186
Voted
BMCBI
2006
170views more  BMCBI 2006»
15 years 6 months ago
Biclustering of gene expression data by non-smooth non-negative matrix factorization
Background: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of gene...
Pedro Carmona-Saez, Roberto D. Pascual-Marqui, Fra...