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BIODATAMINING
2008
96views more  BIODATAMINING 2008»
13 years 8 months ago
Fast approximate hierarchical clustering using similarity heuristics
Background: Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that...
Meelis Kull, Jaak Vilo
ICDE
1999
IEEE
183views Database» more  ICDE 1999»
14 years 10 months ago
ROCK: A Robust Clustering Algorithm for Categorical Attributes
Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) simi...
Sudipto Guha, Rajeev Rastogi, Kyuseok Shim
ICDM
2009
IEEE
112views Data Mining» more  ICDM 2009»
14 years 3 months ago
Resolving Identity Uncertainty with Learned Random Walks
A pervasive problem in large relational databases is identity uncertainty which occurs when multiple entries in a database refer to the same underlying entity in the world. Relati...
Ted Sandler, Lyle H. Ungar, Koby Crammer
ICML
2009
IEEE
14 years 9 months ago
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
Information theoretic based measures form a fundamental class of similarity measures for comparing clusterings, beside the class of pair-counting based and set-matching based meas...
Xuan Vinh Nguyen, Julien Epps, James Bailey
BMCBI
2005
80views more  BMCBI 2005»
13 years 8 months ago
Sample phenotype clusters in high-density oligonucleotide microarray data sets are revealed using Isomap, a nonlinear algorithm
Background: Life processes are determined by the organism's genetic profile and multiple environmental variables. However the interaction between these factors is inherently ...
Kevin Dawson, Raymond L. Rodriguez, Wasyl Malyj