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PRL
2006
77views more  PRL 2006»
13 years 4 months ago
Wavelet based approach to cluster analysis. Application on low dimensional data sets
In this paper, we present a wavelet based approach which tries to automatically find the number of clusters present in a data set, along with their position and statistical proper...
Xavier Otazu, Oriol Pujol
PR
2006
116views more  PR 2006»
13 years 4 months ago
Shared farthest neighbor approach to clustering of high dimensionality, low cardinality data
Clustering algorithms are routinely used in biomedical disciplines, and are a basic tool in bioinformatics. Depending on the task at hand, there are two most popular options, the ...
Stefano Rovetta, Francesco Masulli
BMCBI
2007
123views more  BMCBI 2007»
13 years 5 months ago
Robust clustering in high dimensional data using statistical depths
Background: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor cent...
Yuanyuan Ding, Xin Dang, Hanxiang Peng, Dawn Wilki...
CORR
2006
Springer
146views Education» more  CORR 2006»
13 years 5 months ago
The Haar Wavelet Transform of a Dendrogram
While there is a very long tradition of approximating a data array by projecting row or column vectors into a lower dimensional subspace the direct approximation of a data matrix ...
Fionn Murtagh
KDD
2000
ACM
149views Data Mining» more  KDD 2000»
13 years 8 months ago
Efficient clustering of high-dimensional data sets with application to reference matching
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
Andrew McCallum, Kamal Nigam, Lyle H. Ungar