Sciweavers

142 search results - page 1 / 29
» Clustering Large Datasets in Arbitrary Metric Spaces
Sort
View
152
Voted
ICDE
1999
IEEE
139views Database» more  ICDE 1999»
15 years 11 months ago
Clustering Large Datasets in Arbitrary Metric Spaces
Clustering partitions a collection of objects into groups called clusters, such that similar objects fall into the same group. Similarity between objects is defined by a distance ...
Venkatesh Ganti, Raghu Ramakrishnan, Johannes Gehr...
BMCBI
2010
121views more  BMCBI 2010»
14 years 7 months ago
A grammar-based distance metric enables fast and accurate clustering of large sets of 16S sequences
Background: We propose a sequence clustering algorithm and compare the partition quality and execution time of the proposed algorithm with those of a popular existing algorithm. T...
David J. Russell, Samuel F. Way, Andrew K. Benson,...
STACS
2007
Springer
15 years 3 months ago
Small Space Representations for Metric Min-Sum k -Clustering and Their Applications
The min-sum k-clustering problem is to partition a metric space (P, d) into k clusters C1, . . . , Ck ⊆ P such that k i=1 p,q∈Ci d(p, q) is minimized. We show the first effi...
Artur Czumaj, Christian Sohler
BMCBI
2011
14 years 1 months ago
Clustering gene expression data with a penalized graph-based metric
Background: The search for cluster structure in microarray datasets is a base problem for the so-called “-omic sciences”. A difficult problem in clustering is how to handle da...
Ariel E. Bayá, Pablo M. Granitto
KDD
2002
ACM
155views Data Mining» more  KDD 2002»
15 years 10 months ago
SyMP: an efficient clustering approach to identify clusters of arbitrary shapes in large data sets
We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscill...
Hichem Frigui