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105
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BIODATAMINING
2008
96views more  BIODATAMINING 2008»
15 years 1 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»
16 years 3 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
112
Voted
ICDM
2009
IEEE
112views Data Mining» more  ICDM 2009»
15 years 8 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
101
Voted
ICML
2009
IEEE
16 years 2 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»
15 years 1 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