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» A new approach to data driven clustering
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VTC
2006
IEEE
129views Communications» more  VTC 2006»
14 years 1 months ago
A Framework for Automatic Clustering of Parametric MIMO Channel Data Including Path Powers
— We present a solution to the problem of identifying clusters from MIMO measurement data in a data window, with a minimum of user interaction. Conventionally, visual inspection ...
Nicolai Czink, Pierluigi Cera, Jari Salo, Ernst Bo...
ICASSP
2010
IEEE
13 years 7 months ago
Learning from high-dimensional noisy data via projections onto multi-dimensional ellipsoids
In this paper, we examine the problem of learning from noisecontaminated data in high-dimensional space. A new learning approach based on projections onto multi-dimensional ellips...
Liuling Gong, Dan Schonfeld
BIBE
2004
IEEE
107views Bioinformatics» more  BIBE 2004»
13 years 11 months ago
Enhanced pClustering and Its Applications to Gene Expression Data
Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes an...
Sungroh Yoon, Christine Nardini, Luca Benini, Giov...
ISBI
2009
IEEE
14 years 2 months ago
Image-Driven Population Analysis Through Mixture Modeling
—We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output of...
Mert R. Sabuncu
VLDB
1999
ACM
224views Database» more  VLDB 1999»
13 years 12 months ago
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work e ectively and e ciently in highdimensional sp...
Alexander Hinneburg, Daniel A. Keim