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» Clustering by pattern similarity in large data sets
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BMCBI
2008
142views more  BMCBI 2008»
13 years 7 months ago
Genetic weighted k-means algorithm for clustering large-scale gene expression data
Background: The traditional (unweighted) k-means is one of the most popular clustering methods for analyzing gene expression data. However, it suffers three major shortcomings. It...
Fang-Xiang Wu
EDBT
2004
ACM
172views Database» more  EDBT 2004»
14 years 7 months ago
Efficient Similarity Search for Hierarchical Data in Large Databases
Structured and semi-structured object representations are getting more and more important for modern database applications. Examples for such data are hierarchical structures inclu...
Karin Kailing, Hans-Peter Kriegel, Stefan Schö...
PR
2008
117views more  PR 2008»
13 years 7 months ago
A scale-free distribution of false positives for a large class of audio similarity measures
The "bag-of-frames" approach (BOF) to audio pattern recognition models signals as the long-term statistical distribution of their local spectral features, a prototypical...
Jean-Julien Aucouturier, François Pachet
ICML
2007
IEEE
14 years 8 months ago
Learning distance function by coding similarity
We consider the problem of learning a similarity function from a set of positive equivalence constraints, i.e. 'similar' point pairs. We define the similarity in informa...
Aharon Bar-Hillel, Daphna Weinshall
SSD
2005
Springer
173views Database» more  SSD 2005»
14 years 1 months ago
On Discovering Moving Clusters in Spatio-temporal Data
A moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, a convoy of cars movin...
Panos Kalnis, Nikos Mamoulis, Spiridon Bakiras