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PODS
2005
ACM
115views Database» more  PODS 2005»
16 years 2 months ago
A divide-and-merge methodology for clustering
We present a divide-and-merge methodology for clustering a set of objects that combines a top-down "divide" phase with a bottom-up "merge" phase. In contrast, ...
David Cheng, Santosh Vempala, Ravi Kannan, Grant W...
104
Voted
SETN
2004
Springer
15 years 8 months ago
Incremental Mixture Learning for Clustering Discrete Data
Abstract. This paper elaborates on an efficient approach for clustering discrete data by incrementally building multinomial mixture models through likelihood maximization using the...
Konstantinos Blekas, Aristidis Likas
131
Voted
DKE
1999
118views more  DKE 1999»
15 years 2 months ago
Designing Data Warehouses
A Data Warehouse DW is a database that collects and stores data from multiple remote and heterogeneous information sources. When a query is posed, it is evaluated locally, without...
Dimitri Theodoratos, Timos K. Sellis
105
Voted
ICDE
2006
IEEE
147views Database» more  ICDE 2006»
15 years 8 months ago
Distributing Google
We consider the problem of wide-area large-scale text search over a peer-to-peer infrastructure. A wide-area search infrastructure with billions of documents and millions of searc...
Vijay Gopalakrishnan, Bobby Bhattacharjee, Peter J...
137
Voted
CVPR
2007
IEEE
16 years 4 months ago
Model-Guided Segmentation of 3D Neuroradiological Image Using Statistical Surface Wavelet Model
This paper proposes a novel model-guided segmentation framework utilizing a statistical surface wavelet model as a shape prior. In the model building process, a set of training sh...
Yang Li, Tiow Seng Tan, Ihar Volkau, Wieslaw L. No...