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KAIS
2006
110views more  KAIS 2006»
15 years 6 months ago
Multi-step density-based clustering
Abstract. Data mining in large databases of complex objects from scientific, engineering or multimedia applications is getting more and more important. In many areas, complex dista...
Stefan Brecheisen, Hans-Peter Kriegel, Martin Pfei...
CLUSTER
2007
IEEE
16 years 1 months ago
Performance analysis of a user-level memory server
Abstract—Large-scale parallel applications often produce immense quantities of data that need to be analyzed. To avoid performing repeated, costly disk accesses, analysis of larg...
Scott Pakin, Greg Johnson
CORR
2010
Springer
74views Education» more  CORR 2010»
15 years 7 months ago
Significance of Classification Techniques in Prediction of Learning Disabilities
The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age chil...
Julie M. David, Kannan Balakrishnan
SIGIR
2002
ACM
15 years 6 months ago
Unsupervised document classification using sequential information maximization
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential ...
Noam Slonim, Nir Friedman, Naftali Tishby
BIODATAMINING
2008
96views more  BIODATAMINING 2008»
15 years 7 months ago
Fast approximate hierarchical clustering using similarity heuristics
Background: Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that...
Meelis Kull, Jaak Vilo