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» Discovering Correlations in Annotated Databases
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IVC
2008
69views more  IVC 2008»
13 years 9 months ago
Unsupervised texture classification: Automatically discover and classify texture patterns
In this paper, we present a novel approach to classify texture collections. This approach does not require experts to provide annotated training set. Given the image collection, w...
Lei Qin, Qingfang Zheng, Shuqiang Jiang, Qingming ...
CIBCB
2007
IEEE
14 years 1 months ago
Associative Artificial Neural Network for Discovery of Highly Correlated Gene Groups Based on Gene Ontology and Gene Expression
Abstract-- The advance of high-throughput experimental technologies poses continuous challenges to computational data analysis in functional and comparative genomics studies. Gene ...
Ji He, Xinbin Dai, Xuechun Zhao
SDM
2009
SIAM
114views Data Mining» more  SDM 2009»
14 years 7 months ago
Top-k Correlative Graph Mining.
Correlation mining has been widely studied due to its ability for discovering the underlying occurrence dependency between objects. However, correlation mining in graph databases ...
Yiping Ke, James Cheng, Jeffrey Xu Yu
SAC
2008
ACM
13 years 9 months ago
A clustering-based approach for discovering interesting places in trajectories
Because of the large amount of trajectory data produced by mobile devices, there is an increasing need for mechanisms to extract knowledge from this data. Most existing works have...
Andrey Tietbohl Palma, Vania Bogorny, Bart Kuijper...
BMCBI
2005
126views more  BMCBI 2005»
13 years 9 months ago
HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms
Background: The application of high throughput approaches to the identification of protein interactions has offered for the first time a glimpse of the global interactome of some ...
Maria Persico, Arnaud Ceol, Caius Gavrila, Robert ...