This idea is illustrated in Fig. 2A which shows the populations of ensemble A, B and C in each of the 12 clusters obtained using a common cluster-preference value of −10.
The resulting distances are then used to cluster web pages, where the aim is to group all pages written by the same author in a common cluster.
The feature vectors located in a common cluster is excluded.
We tested the DWCN method and contrasted its results with those obtained using several common clustering techniques on a simulated dataset as well as on two public datasets: the Stanford yeast cell-cycle gene expression data, and a gene expression dataset for a group of genetically different yeast segregants.
Using two common clustering methods, K-means and Spectral techniques, partitioning the hard training patterns database and then the modeling tasks are elaborated.
Importantly, we used a common build of UniGene cluster IDs to find unique gene probes which overlap between the two products.
EST and SNP sequences were clustered using a "common tag" method as previously described [ 7], SNP sequences that contiged with ESTs were, for purposes of this study, assumed to map to exons, and thus designated coding SNPs (cSNPs).
Some of the most common clustering algorithms used in biology are UPGMA [ 20, 34], Neighbor Joining [ 19, 35] and Hierarchical Clustering [ 36, 37], all of which represent their clusters as tree structures.
In order to illustrate the computational power provided by the new parallel processing method, accurate upper bound collapse load estimates, for 3D problems, are produced using a cluster of common PC machines.
Since there are many enriched gene sets that are highly overlapping (sharing common genes), we use a cluster-and-filter strategy to reduce the enriched gene sets.
